Overview

Dataset statistics

Number of variables87
Number of observations415021
Missing cells1497501
Missing cells (%)4.1%
Total size in memory275.5 MiB
Average record size in memory696.0 B

Variable types

Numeric74
Text13

Alerts

vlan_id has constant value ""Constant
bidirectional_cwr_packets has constant value ""Constant
bidirectional_ece_packets has constant value ""Constant
bidirectional_urg_packets has constant value ""Constant
src2dst_cwr_packets has constant value ""Constant
src2dst_ece_packets has constant value ""Constant
src2dst_urg_packets has constant value ""Constant
dst2src_cwr_packets has constant value ""Constant
dst2src_ece_packets has constant value ""Constant
dst2src_urg_packets has constant value ""Constant
requested_server_name has 55644 (13.4%) missing valuesMissing
client_fingerprint has 349959 (84.3%) missing valuesMissing
server_fingerprint has 350487 (84.5%) missing valuesMissing
user_agent has 368761 (88.9%) missing valuesMissing
content_type has 372650 (89.8%) missing valuesMissing
tunnel_id is highly skewed (γ1 = 455.5315576)Skewed
bidirectional_packets is highly skewed (γ1 = 268.234207)Skewed
bidirectional_bytes is highly skewed (γ1 = 289.5768375)Skewed
src2dst_packets is highly skewed (γ1 = 378.4299315)Skewed
src2dst_bytes is highly skewed (γ1 = 415.9170335)Skewed
dst2src_packets is highly skewed (γ1 = 277.9255337)Skewed
dst2src_bytes is highly skewed (γ1 = 345.0878528)Skewed
dst2src_stddev_piat_ms is highly skewed (γ1 = 20.81678377)Skewed
bidirectional_ack_packets is highly skewed (γ1 = 313.4888654)Skewed
bidirectional_psh_packets is highly skewed (γ1 = 243.649542)Skewed
bidirectional_rst_packets is highly skewed (γ1 = 63.0001639)Skewed
src2dst_ack_packets is highly skewed (γ1 = 446.7811648)Skewed
src2dst_psh_packets is highly skewed (γ1 = 317.5344349)Skewed
src2dst_rst_packets is highly skewed (γ1 = 88.53294395)Skewed
dst2src_ack_packets is highly skewed (γ1 = 318.740545)Skewed
dst2src_psh_packets is highly skewed (γ1 = 308.2854443)Skewed
dst2src_rst_packets is highly skewed (γ1 = 41.7481753)Skewed
expiration_id has 413881 (99.7%) zerosZeros
src_port has 17984 (4.3%) zerosZeros
dst_port has 17984 (4.3%) zerosZeros
vlan_id has 415021 (100.0%) zerosZeros
tunnel_id has 415019 (> 99.9%) zerosZeros
bidirectional_duration_ms has 19204 (4.6%) zerosZeros
src2dst_duration_ms has 251285 (60.5%) zerosZeros
dst2src_first_seen_ms has 25599 (6.2%) zerosZeros
dst2src_last_seen_ms has 25599 (6.2%) zerosZeros
dst2src_duration_ms has 249939 (60.2%) zerosZeros
dst2src_packets has 25599 (6.2%) zerosZeros
dst2src_bytes has 25599 (6.2%) zerosZeros
bidirectional_stddev_ps has 29450 (7.1%) zerosZeros
src2dst_stddev_ps has 269545 (64.9%) zerosZeros
dst2src_min_ps has 25599 (6.2%) zerosZeros
dst2src_mean_ps has 25599 (6.2%) zerosZeros
dst2src_stddev_ps has 245007 (59.0%) zerosZeros
dst2src_max_ps has 25599 (6.2%) zerosZeros
bidirectional_min_piat_ms has 168170 (40.5%) zerosZeros
bidirectional_mean_piat_ms has 19204 (4.6%) zerosZeros
bidirectional_stddev_piat_ms has 228976 (55.2%) zerosZeros
bidirectional_max_piat_ms has 19204 (4.6%) zerosZeros
src2dst_min_piat_ms has 311178 (75.0%) zerosZeros
src2dst_mean_piat_ms has 251285 (60.5%) zerosZeros
src2dst_stddev_piat_ms has 268171 (64.6%) zerosZeros
src2dst_max_piat_ms has 251285 (60.5%) zerosZeros
dst2src_min_piat_ms has 344241 (82.9%) zerosZeros
dst2src_mean_piat_ms has 249939 (60.2%) zerosZeros
dst2src_stddev_piat_ms has 282181 (68.0%) zerosZeros
dst2src_max_piat_ms has 249939 (60.2%) zerosZeros
bidirectional_syn_packets has 276440 (66.6%) zerosZeros
bidirectional_cwr_packets has 415021 (100.0%) zerosZeros
bidirectional_ece_packets has 415021 (100.0%) zerosZeros
bidirectional_urg_packets has 415021 (100.0%) zerosZeros
bidirectional_ack_packets has 274857 (66.2%) zerosZeros
bidirectional_psh_packets has 298760 (72.0%) zerosZeros
bidirectional_rst_packets has 385722 (92.9%) zerosZeros
bidirectional_fin_packets has 280364 (67.6%) zerosZeros
src2dst_syn_packets has 276441 (66.6%) zerosZeros
src2dst_cwr_packets has 415021 (100.0%) zerosZeros
src2dst_ece_packets has 415021 (100.0%) zerosZeros
src2dst_urg_packets has 415021 (100.0%) zerosZeros
src2dst_ack_packets has 276261 (66.6%) zerosZeros
src2dst_psh_packets has 299459 (72.2%) zerosZeros
src2dst_rst_packets has 390954 (94.2%) zerosZeros
src2dst_fin_packets has 283006 (68.2%) zerosZeros
dst2src_syn_packets has 279978 (67.5%) zerosZeros
dst2src_cwr_packets has 415021 (100.0%) zerosZeros
dst2src_ece_packets has 415021 (100.0%) zerosZeros
dst2src_urg_packets has 415021 (100.0%) zerosZeros
dst2src_ack_packets has 275043 (66.3%) zerosZeros
dst2src_psh_packets has 300069 (72.3%) zerosZeros
dst2src_rst_packets has 400230 (96.4%) zerosZeros
dst2src_fin_packets has 286835 (69.1%) zerosZeros
application_is_guessed has 388080 (93.5%) zerosZeros
label has 413630 (99.7%) zerosZeros

Reproduction

Analysis started2023-09-28 17:57:25.714349
Analysis finished2023-09-28 17:57:43.733215
Duration18.02 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct74530
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23762.94479
Minimum0
Maximum74529
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:44.419429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1294
Q17470
median19077
Q336644
95-th percentile62103
Maximum74529
Range74529
Interquartile range (IQR)29174

Descriptive statistics

Standard deviation18987.07879
Coefficient of variation (CV)0.799020448
Kurtosis-0.42516105
Mean23762.94479
Median Absolute Deviation (MAD)13429
Skewness0.730804221
Sum9862121108
Variance360509161
MonotonicityNot monotonic
2023-09-28T14:57:44.688061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
< 0.1%
24 17
 
< 0.1%
26 17
 
< 0.1%
27 17
 
< 0.1%
28 17
 
< 0.1%
29 17
 
< 0.1%
30 17
 
< 0.1%
31 17
 
< 0.1%
32 17
 
< 0.1%
33 17
 
< 0.1%
Other values (74520) 414851
> 99.9%
ValueCountFrequency (%)
0 17
< 0.1%
1 17
< 0.1%
2 17
< 0.1%
3 17
< 0.1%
4 17
< 0.1%
ValueCountFrequency (%)
74529 1
< 0.1%
74528 1
< 0.1%
74527 1
< 0.1%
74526 1
< 0.1%
74525 1
< 0.1%

expiration_id
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002746848955
Minimum0
Maximum1
Zeros413881
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:45.025162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0523384216
Coefficient of variation (CV)19.05398603
Kurtosis359.0606034
Mean0.002746848955
Median Absolute Deviation (MAD)0
Skewness19.00154923
Sum1140
Variance0.002739310376
MonotonicityNot monotonic
2023-09-28T14:57:45.287690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 413881
99.7%
1 1140
 
0.3%
ValueCountFrequency (%)
0 413881
99.7%
1 1140
 
0.3%
ValueCountFrequency (%)
1 1140
 
0.3%
0 413881
99.7%

src_ip
Text

Distinct873
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:46.060198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length9
Mean length10.58169828
Min length2

Characters and Unicode

Total characters4391627
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique354 ?
Unique (%)0.1%

Sample

1st row192.168.1.111
2nd row192.168.1.111
3rd rowfe80::6529:a551:88b9:f0ca
4th row192.168.1.191
5th row192.168.1.191
ValueCountFrequency (%)
10.0.2.15 281264
67.8%
192.168.1.191 81248
 
19.6%
192.168.1.193 5514
 
1.3%
192.168.1.192 5171
 
1.2%
10.0.0.46 5096
 
1.2%
fe80::16cc:20ff:fe51:33ea 4363
 
1.1%
192.168.1.240 4200
 
1.0%
fe80::725a:fff:fee4:9bc0 3387
 
0.8%
192.168.1.249 2534
 
0.6%
10.0.0.34 1787
 
0.4%
Other values (863) 20457
 
4.9%
2023-09-28T14:57:46.735908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1205235
27.4%
1 1103012
25.1%
0 616156
14.0%
2 429514
 
9.8%
5 297385
 
6.8%
9 216191
 
4.9%
8 133222
 
3.0%
6 128417
 
2.9%
: 66312
 
1.5%
f 56993
 
1.3%
Other values (8) 139190
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2988609
68.1%
Other Punctuation 1271547
29.0%
Lowercase Letter 131471
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1103012
36.9%
0 616156
20.6%
2 429514
 
14.4%
5 297385
 
10.0%
9 216191
 
7.2%
8 133222
 
4.5%
6 128417
 
4.3%
3 28473
 
1.0%
4 26723
 
0.9%
7 9516
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
f 56993
43.4%
e 42461
32.3%
c 13220
 
10.1%
a 11517
 
8.8%
b 5843
 
4.4%
d 1437
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 1205235
94.8%
: 66312
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4260156
97.0%
Latin 131471
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1205235
28.3%
1 1103012
25.9%
0 616156
14.5%
2 429514
 
10.1%
5 297385
 
7.0%
9 216191
 
5.1%
8 133222
 
3.1%
6 128417
 
3.0%
: 66312
 
1.6%
3 28473
 
0.7%
Other values (2) 36239
 
0.9%
Latin
ValueCountFrequency (%)
f 56993
43.4%
e 42461
32.3%
c 13220
 
10.1%
a 11517
 
8.8%
b 5843
 
4.4%
d 1437
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4391627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1205235
27.4%
1 1103012
25.1%
0 616156
14.0%
2 429514
 
9.8%
5 297385
 
6.8%
9 216191
 
4.9%
8 133222
 
3.0%
6 128417
 
2.9%
: 66312
 
1.5%
f 56993
 
1.3%
Other values (8) 139190
 
3.2%
Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:47.057856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters7055357
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2c:6e:85:56:dd:b7
2nd row2c:6e:85:56:dd:b7
3rd row2c:6e:85:56:dd:b7
4th row60:6c:66:cb:78:61
5th row60:6c:66:cb:78:61
ValueCountFrequency (%)
08:00:27:a3:83:43 281597
67.9%
60:6c:66:cb:78:61 81248
 
19.6%
78:e4:00:6c:39:cd 6883
 
1.7%
ec:1a:59:83:28:11 6831
 
1.6%
00:62:6e:51:27:2e 5237
 
1.3%
14:cc:20:51:33:ea 4954
 
1.2%
70:5a:0f:e4:9b:c0 4220
 
1.0%
44:65:0d:56:cc:d3 4200
 
1.0%
00:16:6c:ab:6b:88 3483
 
0.8%
00:13:33:b0:18:50 3381
 
0.8%
Other values (28) 12987
 
3.1%
2023-09-28T14:57:47.529087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 2075105
29.4%
0 1011220
14.3%
3 895086
12.7%
8 688103
 
9.8%
6 446155
 
6.3%
7 388391
 
5.5%
4 319608
 
4.5%
2 316207
 
4.5%
a 305786
 
4.3%
c 214864
 
3.0%
Other values (7) 394832
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4279187
60.7%
Other Punctuation 2075105
29.4%
Lowercase Letter 701065
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1011220
23.6%
3 895086
20.9%
8 688103
16.1%
6 446155
10.4%
7 388391
 
9.1%
4 319608
 
7.5%
2 316207
 
7.4%
1 140652
 
3.3%
5 44011
 
1.0%
9 29754
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
a 305786
43.6%
c 214864
30.6%
b 99536
 
14.2%
e 46711
 
6.7%
d 20350
 
2.9%
f 13818
 
2.0%
Other Punctuation
ValueCountFrequency (%)
: 2075105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6354292
90.1%
Latin 701065
 
9.9%

Most frequent character per script

Common
ValueCountFrequency (%)
: 2075105
32.7%
0 1011220
15.9%
3 895086
14.1%
8 688103
 
10.8%
6 446155
 
7.0%
7 388391
 
6.1%
4 319608
 
5.0%
2 316207
 
5.0%
1 140652
 
2.2%
5 44011
 
0.7%
Latin
ValueCountFrequency (%)
a 305786
43.6%
c 214864
30.6%
b 99536
 
14.2%
e 46711
 
6.7%
d 20350
 
2.9%
f 13818
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7055357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 2075105
29.4%
0 1011220
14.3%
3 895086
12.7%
8 688103
 
9.8%
6 446155
 
6.3%
7 388391
 
5.5%
4 319608
 
4.5%
2 316207
 
4.5%
a 305786
 
4.3%
c 214864
 
3.0%
Other values (7) 394832
 
5.6%
Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:47.816414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3320168
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2c:6e:85
2nd row2c:6e:85
3rd row2c:6e:85
4th row60:6c:66
5th row60:6c:66
ValueCountFrequency (%)
08:00:27 281640
67.9%
60:6c:66 81248
 
19.6%
ec:1a:59 9828
 
2.4%
78:e4:00 6883
 
1.7%
00:62:6e 5237
 
1.3%
14:cc:20 4954
 
1.2%
70:5a:0f 4220
 
1.0%
44:65:0d 4200
 
1.0%
00:16:6c 3483
 
0.8%
00:13:33 3381
 
0.8%
Other values (23) 9947
 
2.4%
2023-09-28T14:57:48.289637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 994047
29.9%
: 830042
25.0%
6 348667
 
10.5%
7 298293
 
9.0%
2 296206
 
8.9%
8 290696
 
8.8%
c 105317
 
3.2%
e 30635
 
0.9%
4 24784
 
0.7%
5 23236
 
0.7%
Other values (7) 78245
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2321276
69.9%
Other Punctuation 830042
 
25.0%
Lowercase Letter 168850
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 994047
42.8%
6 348667
 
15.0%
7 298293
 
12.9%
2 296206
 
12.8%
8 290696
 
12.5%
4 24784
 
1.1%
5 23236
 
1.0%
1 23123
 
1.0%
3 11690
 
0.5%
9 10534
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
c 105317
62.4%
e 30635
 
18.1%
a 14505
 
8.6%
d 8816
 
5.2%
f 8438
 
5.0%
b 1139
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 830042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3151318
94.9%
Latin 168850
 
5.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 994047
31.5%
: 830042
26.3%
6 348667
 
11.1%
7 298293
 
9.5%
2 296206
 
9.4%
8 290696
 
9.2%
4 24784
 
0.8%
5 23236
 
0.7%
1 23123
 
0.7%
3 11690
 
0.4%
Latin
ValueCountFrequency (%)
c 105317
62.4%
e 30635
 
18.1%
a 14505
 
8.6%
d 8816
 
5.2%
f 8438
 
5.0%
b 1139
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3320168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 994047
29.9%
: 830042
25.0%
6 348667
 
10.5%
7 298293
 
9.0%
2 296206
 
8.9%
8 290696
 
8.8%
c 105317
 
3.2%
e 30635
 
0.9%
4 24784
 
0.7%
5 23236
 
0.7%
Other values (7) 78245
 
2.4%

src_port
Real number (ℝ)

Distinct33761
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50224.94064
Minimum0
Maximum65535
Zeros17984
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:48.724309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q149666
median54275
Q359223
95-th percentile64027
Maximum65535
Range65535
Interquartile range (IQR)9557

Descriptive statistics

Standard deviation15577.19743
Coefficient of variation (CV)0.3101486478
Kurtosis4.4788142
Mean50224.94064
Median Absolute Deviation (MAD)4761
Skewness-2.236195297
Sum2.084440509 × 1010
Variance242649079.7
MonotonicityNot monotonic
2023-09-28T14:57:48.974894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17984
 
4.3%
59370 2112
 
0.5%
80 1496
 
0.4%
53 729
 
0.2%
443 650
 
0.2%
1900 593
 
0.1%
10001 591
 
0.1%
3080 568
 
0.1%
68 443
 
0.1%
59960 359
 
0.1%
Other values (33751) 389496
93.8%
ValueCountFrequency (%)
0 17984
4.3%
53 729
 
0.2%
67 149
 
< 0.1%
68 443
 
0.1%
80 1496
 
0.4%
ValueCountFrequency (%)
65535 10
< 0.1%
65534 13
< 0.1%
65533 9
< 0.1%
65532 12
< 0.1%
65531 12
< 0.1%

dst_ip
Text

Distinct11627
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:49.644609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length14
Mean length13.40992865
Min length7

Characters and Unicode

Total characters5565402
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2127 ?
Unique (%)0.5%

Sample

1st row239.255.255.250
2nd row224.0.0.251
3rd rowff02::fb
4th row192.168.33.254
5th row192.168.33.254
ValueCountFrequency (%)
192.168.33.254 221606
53.4%
208.91.112.53 12944
 
3.1%
8.8.8.8 5315
 
1.3%
192.168.1.1 4852
 
1.2%
23.51.123.27 3096
 
0.7%
192.168.1.223 3020
 
0.7%
239.255.255.250 2731
 
0.7%
192.168.1.191 2731
 
0.7%
192.168.1.193 1733
 
0.4%
ff02::2 1313
 
0.3%
Other values (11617) 155680
37.5%
2023-09-28T14:57:50.566465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1205235
21.7%
1 881999
15.8%
2 790584
14.2%
3 592155
10.6%
5 379170
 
6.8%
8 356929
 
6.4%
9 350392
 
6.3%
4 342582
 
6.2%
6 323608
 
5.8%
0 139691
 
2.5%
Other values (8) 203057
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4264434
76.6%
Other Punctuation 1248401
 
22.4%
Lowercase Letter 52567
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 881999
20.7%
2 790584
18.5%
3 592155
13.9%
5 379170
8.9%
8 356929
8.4%
9 350392
 
8.2%
4 342582
 
8.0%
6 323608
 
7.6%
0 139691
 
3.3%
7 107324
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
f 42204
80.3%
e 3544
 
6.7%
c 2823
 
5.4%
b 2604
 
5.0%
a 1318
 
2.5%
d 74
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1205235
96.5%
: 43166
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5512835
99.1%
Latin 52567
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1205235
21.9%
1 881999
16.0%
2 790584
14.3%
3 592155
10.7%
5 379170
 
6.9%
8 356929
 
6.5%
9 350392
 
6.4%
4 342582
 
6.2%
6 323608
 
5.9%
0 139691
 
2.5%
Other values (2) 150490
 
2.7%
Latin
ValueCountFrequency (%)
f 42204
80.3%
e 3544
 
6.7%
c 2823
 
5.4%
b 2604
 
5.0%
a 1318
 
2.5%
d 74
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5565402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1205235
21.7%
1 881999
15.8%
2 790584
14.2%
3 592155
10.6%
5 379170
 
6.8%
8 356929
 
6.4%
9 350392
 
6.3%
4 342582
 
6.2%
6 323608
 
5.8%
0 139691
 
2.5%
Other values (8) 203057
 
3.6%
Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:50.855497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters7055357
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row01:00:5e:7f:ff:fa
2nd row01:00:5e:00:00:fb
3rd row33:33:00:00:00:fb
4th row00:13:33:b0:18:50
5th row00:13:33:b0:18:50
ValueCountFrequency (%)
52:54:00:12:35:02 281177
67.8%
00:13:33:b0:18:50 81248
 
19.6%
14:cc:20:51:33:ea 16281
 
3.9%
38:72:c0:5e:6b:22 6881
 
1.7%
ec:1a:59:79:f4:89 3020
 
0.7%
60:6c:66:cb:78:61 2731
 
0.7%
01:00:5e:7f:ff:fa 2731
 
0.7%
ec:1a:59:83:28:11 1733
 
0.4%
33:33:00:00:00:02 1313
 
0.3%
00:16:6c:ab:6b:88 1311
 
0.3%
Other values (53) 16595
 
4.0%
2023-09-28T14:57:51.301379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 2075105
29.4%
0 1275579
18.1%
5 958628
13.6%
2 887207
12.6%
3 626083
 
8.9%
1 501595
 
7.1%
4 304505
 
4.3%
8 105649
 
1.5%
b 97713
 
1.4%
c 55888
 
0.8%
Other values (7) 167405
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4715371
66.8%
Other Punctuation 2075105
29.4%
Lowercase Letter 264881
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1275579
27.1%
5 958628
20.3%
2 887207
18.8%
3 626083
13.3%
1 501595
 
10.6%
4 304505
 
6.5%
8 105649
 
2.2%
6 25922
 
0.5%
7 16588
 
0.4%
9 13615
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
b 97713
36.9%
c 55888
21.1%
f 45919
17.3%
e 38194
 
14.4%
a 26597
 
10.0%
d 570
 
0.2%
Other Punctuation
ValueCountFrequency (%)
: 2075105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6790476
96.2%
Latin 264881
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
: 2075105
30.6%
0 1275579
18.8%
5 958628
14.1%
2 887207
13.1%
3 626083
 
9.2%
1 501595
 
7.4%
4 304505
 
4.5%
8 105649
 
1.6%
6 25922
 
0.4%
7 16588
 
0.2%
Latin
ValueCountFrequency (%)
b 97713
36.9%
c 55888
21.1%
f 45919
17.3%
e 38194
 
14.4%
a 26597
 
10.0%
d 570
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7055357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 2075105
29.4%
0 1275579
18.1%
5 958628
13.6%
2 887207
12.6%
3 626083
 
8.9%
1 501595
 
7.1%
4 304505
 
4.3%
8 105649
 
1.5%
b 97713
 
1.4%
c 55888
 
0.8%
Other values (7) 167405
 
2.4%
Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:51.553912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3320168
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row01:00:5e
2nd row01:00:5e
3rd row33:33:00
4th row00:13:33
5th row00:13:33
ValueCountFrequency (%)
52:54:00 281177
67.8%
00:13:33 81248
 
19.6%
14:cc:20 16281
 
3.9%
33:33:ff 7095
 
1.7%
38:72:c0 6881
 
1.7%
33:33:00 6096
 
1.5%
01:00:5e 4754
 
1.1%
ec:1a:59 4753
 
1.1%
60:6c:66 2731
 
0.7%
00:16:6c 1311
 
0.3%
Other values (19) 2694
 
0.6%
2023-09-28T14:57:52.011164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 830042
25.0%
0 781731
23.5%
5 572237
17.2%
2 305186
 
9.2%
3 303462
 
9.1%
4 297967
 
9.0%
1 109098
 
3.3%
c 48309
 
1.5%
f 22145
 
0.7%
6 13770
 
0.4%
Other values (7) 36221
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2403628
72.4%
Other Punctuation 830042
 
25.0%
Lowercase Letter 86498
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 781731
32.5%
5 572237
23.8%
2 305186
 
12.7%
3 303462
 
12.6%
4 297967
 
12.4%
1 109098
 
4.5%
6 13770
 
0.6%
8 7912
 
0.3%
7 7474
 
0.3%
9 4791
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
c 48309
55.8%
f 22145
25.6%
e 10967
 
12.7%
a 4763
 
5.5%
d 229
 
0.3%
b 85
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 830042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3233670
97.4%
Latin 86498
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
: 830042
25.7%
0 781731
24.2%
5 572237
17.7%
2 305186
 
9.4%
3 303462
 
9.4%
4 297967
 
9.2%
1 109098
 
3.4%
6 13770
 
0.4%
8 7912
 
0.2%
7 7474
 
0.2%
Latin
ValueCountFrequency (%)
c 48309
55.8%
f 22145
25.6%
e 10967
 
12.7%
a 4763
 
5.5%
d 229
 
0.3%
b 85
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3320168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 830042
25.0%
0 781731
23.5%
5 572237
17.2%
2 305186
 
9.2%
3 303462
 
9.1%
4 297967
 
9.0%
1 109098
 
3.3%
c 48309
 
1.5%
f 22145
 
0.7%
6 13770
 
0.4%
Other values (7) 36221
 
1.1%

dst_port
Real number (ℝ)

Distinct4066
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1475.291718
Minimum0
Maximum65502
Zeros17984
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:52.267294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53
Q153
median53
Q380
95-th percentile443
Maximum65502
Range65502
Interquartile range (IQR)27

Descriptive statistics

Standard deviation7761.824541
Coefficient of variation (CV)5.261213391
Kurtosis34.53478719
Mean1475.291718
Median Absolute Deviation (MAD)0
Skewness5.962975083
Sum612277044
Variance60245920.2
MonotonicityNot monotonic
2023-09-28T14:57:52.508541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 244095
58.8%
443 66124
 
15.9%
80 64637
 
15.6%
0 17984
 
4.3%
49153 3479
 
0.8%
123 3031
 
0.7%
1900 1520
 
0.4%
49152 1313
 
0.3%
3080 1274
 
0.3%
3478 961
 
0.2%
Other values (4056) 10603
 
2.6%
ValueCountFrequency (%)
0 17984
 
4.3%
7 1
 
< 0.1%
22 1
 
< 0.1%
53 244095
58.8%
67 443
 
0.1%
ValueCountFrequency (%)
65502 1
< 0.1%
65173 2
< 0.1%
65035 2
< 0.1%
64964 2
< 0.1%
64913 2
< 0.1%

protocol
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.34910041
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:52.728789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median17
Q317
95-th percentile17
Maximum58
Range57
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.435613684
Coefficient of variation (CV)0.6575752776
Kurtosis11.44593123
Mean14.34910041
Median Absolute Deviation (MAD)0
Skewness2.816134462
Sum5955178
Variance89.03080559
MonotonicityNot monotonic
2023-09-28T14:57:52.977996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
17 256253
61.7%
6 140784
33.9%
58 12860
 
3.1%
2 3169
 
0.8%
1 1955
 
0.5%
ValueCountFrequency (%)
1 1955
 
0.5%
2 3169
 
0.8%
6 140784
33.9%
17 256253
61.7%
58 12860
 
3.1%
ValueCountFrequency (%)
58 12860
 
3.1%
17 256253
61.7%
6 140784
33.9%
2 3169
 
0.8%
1 1955
 
0.5%

ip_version
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.063977485
Minimum4
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:53.170690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum6
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35194055
Coefficient of variation (CV)0.08660002455
Kurtosis26.29437437
Mean4.063977485
Median Absolute Deviation (MAD)0
Skewness5.319233747
Sum1686636
Variance0.1238621508
MonotonicityNot monotonic
2023-09-28T14:57:53.384432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
4 401745
96.8%
6 13276
 
3.2%
ValueCountFrequency (%)
4 401745
96.8%
6 13276
 
3.2%
ValueCountFrequency (%)
6 13276
 
3.2%
4 401745
96.8%

vlan_id
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:53.585480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:57:53.797700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

tunnel_id
Real number (ℝ)

SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.445709976 × 10-5
Minimum0
Maximum3
Zeros415019
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:53.969480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.006585681041
Coefficient of variation (CV)455.5326552
Kurtosis207508
Mean1.445709976 × 10-5
Median Absolute Deviation (MAD)0
Skewness455.5315576
Sum6
Variance4.337119478 × 10-5
MonotonicityNot monotonic
2023-09-28T14:57:54.155753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 415019
> 99.9%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 415019
> 99.9%
3 2
 
< 0.1%
ValueCountFrequency (%)
3 2
 
< 0.1%
0 415019
> 99.9%
Distinct374519
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.764565004 × 1011
Minimum7392
Maximum1.493732977 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:54.389801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7392
5-th percentile834478
Q14493928
median10003370
Q31.47520767 × 1012
95-th percentile1.49373139 × 1012
Maximum1.493732977 × 1012
Range1.493732969 × 1012
Interquartile range (IQR)1.475203176 × 1012

Descriptive statistics

Standard deviation6.925793423 × 1011
Coefficient of variation (CV)1.453604562
Kurtosis-1.411299278
Mean4.764565004 × 1011
Median Absolute Deviation (MAD)7241276
Skewness0.7663701723
Sum1.977394532 × 1017
Variance4.796661454 × 1023
MonotonicityNot monotonic
2023-09-28T14:57:54.665294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.493730733 × 101228
 
< 0.1%
1.493730732 × 101228
 
< 0.1%
1.493729245 × 101227
 
< 0.1%
1.49373175 × 101226
 
< 0.1%
1.493731159 × 101226
 
< 0.1%
1.493730983 × 101224
 
< 0.1%
1.493729262 × 101223
 
< 0.1%
1.493730652 × 101223
 
< 0.1%
1.493731092 × 101223
 
< 0.1%
1.493729261 × 101222
 
< 0.1%
Other values (374509) 414771
99.9%
ValueCountFrequency (%)
7392 1
< 0.1%
8122 1
< 0.1%
13080 1
< 0.1%
13100 1
< 0.1%
13121 1
< 0.1%
ValueCountFrequency (%)
1.493732977 × 10121
< 0.1%
1.493732973 × 10121
< 0.1%
1.493732972 × 10121
< 0.1%
1.493732961 × 10121
< 0.1%
1.49373296 × 10121
< 0.1%
Distinct384439
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.764565248 × 1011
Minimum13099
Maximum1.49373298 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:54.949506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13099
5-th percentile851388
Q14517829
median10030427
Q31.475207729 × 1012
95-th percentile1.493731402 × 1012
Maximum1.49373298 × 1012
Range1.493732967 × 1012
Interquartile range (IQR)1.475203212 × 1012

Descriptive statistics

Standard deviation6.925793458 × 1011
Coefficient of variation (CV)1.453604494
Kurtosis-1.411299279
Mean4.764565248 × 1011
Median Absolute Deviation (MAD)7241896
Skewness0.7663701719
Sum1.977394634 × 1017
Variance4.796661502 × 1023
MonotonicityNot monotonic
2023-09-28T14:57:55.251737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.493728105 × 101221
 
< 0.1%
1.493728071 × 101217
 
< 0.1%
1.493728087 × 101216
 
< 0.1%
1.493731028 × 101215
 
< 0.1%
1.493727927 × 101214
 
< 0.1%
1.493728072 × 101214
 
< 0.1%
1.493727927 × 101212
 
< 0.1%
1.38731541 × 101212
 
< 0.1%
1.493730472 × 101212
 
< 0.1%
1.493727961 × 101212
 
< 0.1%
Other values (384429) 414876
> 99.9%
ValueCountFrequency (%)
13099 1
< 0.1%
13119 1
< 0.1%
13139 1
< 0.1%
13158 1
< 0.1%
14160 1
< 0.1%
ValueCountFrequency (%)
1.49373298 × 10121
< 0.1%
1.493732979 × 10121
< 0.1%
1.493732973 × 10121
< 0.1%
1.493732973 × 10121
< 0.1%
1.493732969 × 10121
< 0.1%

bidirectional_duration_ms
Real number (ℝ)

Distinct59792
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24432.84857
Minimum0
Maximum1799999
Zeros19204
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:55.551896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median58
Q35291
95-th percentile125957
Maximum1799999
Range1799999
Interquartile range (IQR)5267

Descriptive statistics

Standard deviation113749.7489
Coefficient of variation (CV)4.655607332
Kurtosis175.5630404
Mean24432.84857
Median Absolute Deviation (MAD)55
Skewness12.10771043
Sum1.014014524 × 1010
Variance1.293900538 × 1010
MonotonicityNot monotonic
2023-09-28T14:57:55.964361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19204
 
4.6%
24 5930
 
1.4%
25 5930
 
1.4%
23 5892
 
1.4%
26 5829
 
1.4%
27 5699
 
1.4%
28 5616
 
1.4%
22 5515
 
1.3%
29 5482
 
1.3%
21 5446
 
1.3%
Other values (59782) 344478
83.0%
ValueCountFrequency (%)
0 19204
4.6%
1 916
 
0.2%
2 1031
 
0.2%
3 1468
 
0.4%
4 1415
 
0.3%
ValueCountFrequency (%)
1799999 6
< 0.1%
1799998 1
 
< 0.1%
1799997 1
 
< 0.1%
1799996 2
 
< 0.1%
1799995 3
< 0.1%

bidirectional_packets
Real number (ℝ)

Distinct1775
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.0335742
Minimum1
Maximum392213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:56.277367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q314
95-th percentile72
Maximum392213
Range392212
Interquartile range (IQR)12

Descriptive statistics

Standard deviation971.0409583
Coefficient of variation (CV)33.44545014
Kurtosis88391.43947
Mean29.0335742
Median Absolute Deviation (MAD)0
Skewness268.234207
Sum12049543
Variance942920.5427
MonotonicityNot monotonic
2023-09-28T14:57:56.574570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 211032
50.8%
4 36388
 
8.8%
1 17705
 
4.3%
7 12559
 
3.0%
6 10030
 
2.4%
14 6377
 
1.5%
3 5226
 
1.3%
10 4641
 
1.1%
33 4250
 
1.0%
12 3563
 
0.9%
Other values (1765) 103250
24.9%
ValueCountFrequency (%)
1 17705
 
4.3%
2 211032
50.8%
3 5226
 
1.3%
4 36388
 
8.8%
5 2476
 
0.6%
ValueCountFrequency (%)
392213 1
< 0.1%
275466 1
< 0.1%
184518 2
< 0.1%
124890 2
< 0.1%
110611 2
< 0.1%

bidirectional_bytes
Real number (ℝ)

Distinct39429
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18598.70422
Minimum46
Maximum424668890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:56.923689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile162
Q1240
median404
Q31913
95-th percentile28495
Maximum424668890
Range424668844
Interquartile range (IQR)1673

Descriptive statistics

Standard deviation1002774.218
Coefficient of variation (CV)53.9163485
Kurtosis103462.2499
Mean18598.70422
Median Absolute Deviation (MAD)206
Skewness289.5768375
Sum7718852823
Variance1.005556133 × 1012
MonotonicityNot monotonic
2023-09-28T14:57:57.177118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 11501
 
2.8%
408 6420
 
1.5%
390 5933
 
1.4%
394 5187
 
1.2%
180 4471
 
1.1%
320 4242
 
1.0%
166 4211
 
1.0%
228 4016
 
1.0%
225 3766
 
0.9%
188 3606
 
0.9%
Other values (39419) 361668
87.1%
ValueCountFrequency (%)
46 2383
0.6%
54 7
 
< 0.1%
55 96
 
< 0.1%
60 544
 
0.1%
62 77
 
< 0.1%
ValueCountFrequency (%)
424668890 1
< 0.1%
307383769 1
< 0.1%
148005382 2
< 0.1%
128190733 2
< 0.1%
110825666 2
< 0.1%

src2dst_first_seen_ms
Real number (ℝ)

Distinct374519
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.764565004 × 1011
Minimum7392
Maximum1.493732977 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:57.473751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7392
5-th percentile834478
Q14493928
median10003370
Q31.47520767 × 1012
95-th percentile1.49373139 × 1012
Maximum1.493732977 × 1012
Range1.493732969 × 1012
Interquartile range (IQR)1.475203176 × 1012

Descriptive statistics

Standard deviation6.925793423 × 1011
Coefficient of variation (CV)1.453604562
Kurtosis-1.411299278
Mean4.764565004 × 1011
Median Absolute Deviation (MAD)7241276
Skewness0.7663701723
Sum1.977394532 × 1017
Variance4.796661454 × 1023
MonotonicityNot monotonic
2023-09-28T14:57:57.766122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.493730733 × 101228
 
< 0.1%
1.493730732 × 101228
 
< 0.1%
1.493729245 × 101227
 
< 0.1%
1.49373175 × 101226
 
< 0.1%
1.493731159 × 101226
 
< 0.1%
1.493730983 × 101224
 
< 0.1%
1.493729262 × 101223
 
< 0.1%
1.493730652 × 101223
 
< 0.1%
1.493731092 × 101223
 
< 0.1%
1.493729261 × 101222
 
< 0.1%
Other values (374509) 414771
99.9%
ValueCountFrequency (%)
7392 1
< 0.1%
8122 1
< 0.1%
13080 1
< 0.1%
13100 1
< 0.1%
13121 1
< 0.1%
ValueCountFrequency (%)
1.493732977 × 10121
< 0.1%
1.493732973 × 10121
< 0.1%
1.493732972 × 10121
< 0.1%
1.493732961 × 10121
< 0.1%
1.49373296 × 10121
< 0.1%

src2dst_last_seen_ms
Real number (ℝ)

Distinct389104
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.764565247 × 1011
Minimum13080
Maximum1.49373298 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:58.088432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13080
5-th percentile851345
Q14517758
median10030409
Q31.475207729 × 1012
95-th percentile1.493731401 × 1012
Maximum1.49373298 × 1012
Range1.493732967 × 1012
Interquartile range (IQR)1.475203212 × 1012

Descriptive statistics

Standard deviation6.925793458 × 1011
Coefficient of variation (CV)1.453604495
Kurtosis-1.411299279
Mean4.764565247 × 1011
Median Absolute Deviation (MAD)7242035
Skewness0.7663701719
Sum1.977394633 × 1017
Variance4.796661502 × 1023
MonotonicityNot monotonic
2023-09-28T14:57:58.359353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.49372988 × 101224
 
< 0.1%
1.493728105 × 101223
 
< 0.1%
1.493727967 × 101220
 
< 0.1%
1.493728803 × 101218
 
< 0.1%
1.49373094 × 101217
 
< 0.1%
1.49373094 × 101217
 
< 0.1%
1.387315073 × 101216
 
< 0.1%
1.493730737 × 101216
 
< 0.1%
1.493731028 × 101215
 
< 0.1%
1.493728087 × 101215
 
< 0.1%
Other values (389094) 414840
> 99.9%
ValueCountFrequency (%)
13080 1
< 0.1%
13100 1
< 0.1%
13121 1
< 0.1%
13139 1
< 0.1%
14160 1
< 0.1%
ValueCountFrequency (%)
1.49373298 × 10121
< 0.1%
1.493732979 × 10121
< 0.1%
1.493732973 × 10121
< 0.1%
1.493732973 × 10121
< 0.1%
1.493732969 × 10121
< 0.1%

src2dst_duration_ms
Real number (ℝ)

Distinct59505
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24346.9568
Minimum0
Maximum1799999
Zeros251285
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:58.633527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35164
95-th percentile125933
Maximum1799999
Range1799999
Interquartile range (IQR)5164

Descriptive statistics

Standard deviation113734.7812
Coefficient of variation (CV)4.671416726
Kurtosis175.5619908
Mean24346.9568
Median Absolute Deviation (MAD)0
Skewness12.10750328
Sum1.010449836 × 1010
Variance1.293560046 × 1010
MonotonicityNot monotonic
2023-09-28T14:57:58.907637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 251285
60.5%
1 1417
 
0.3%
206 214
 
0.1%
19 176
 
< 0.1%
218 175
 
< 0.1%
18 174
 
< 0.1%
15 171
 
< 0.1%
107 166
 
< 0.1%
22 165
 
< 0.1%
103 165
 
< 0.1%
Other values (59495) 160913
38.8%
ValueCountFrequency (%)
0 251285
60.5%
1 1417
 
0.3%
2 158
 
< 0.1%
3 113
 
< 0.1%
4 82
 
< 0.1%
ValueCountFrequency (%)
1799999 6
< 0.1%
1799998 1
 
< 0.1%
1799997 1
 
< 0.1%
1799996 1
 
< 0.1%
1799995 2
 
< 0.1%

src2dst_packets
Real number (ℝ)

Distinct848
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.59281338
Minimum1
Maximum271835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:59.209862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q37
95-th percentile31
Maximum271835
Range271834
Interquartile range (IQR)6

Descriptive statistics

Standard deviation532.5570142
Coefficient of variation (CV)45.93854804
Kurtosis173471.0738
Mean11.59281338
Median Absolute Deviation (MAD)0
Skewness378.4299315
Sum4811261
Variance283616.9734
MonotonicityNot monotonic
2023-09-28T14:57:59.489805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 224977
54.2%
2 42818
 
10.3%
4 21868
 
5.3%
7 9028
 
2.2%
10 8667
 
2.1%
17 7534
 
1.8%
8 6955
 
1.7%
11 6881
 
1.7%
9 5926
 
1.4%
6 5562
 
1.3%
Other values (838) 74805
 
18.0%
ValueCountFrequency (%)
1 224977
54.2%
2 42818
 
10.3%
3 5468
 
1.3%
4 21868
 
5.3%
5 4952
 
1.2%
ValueCountFrequency (%)
271835 1
< 0.1%
97615 2
< 0.1%
91724 2
< 0.1%
42453 1
< 0.1%
37225 1
< 0.1%

src2dst_bytes
Real number (ℝ)

Distinct14881
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3693.181796
Minimum46
Maximum383809522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:57:59.784170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile72
Q180
median93
Q3895
95-th percentile4480
Maximum383809522
Range383809476
Interquartile range (IQR)815

Descriptive statistics

Standard deviation724639.3667
Coefficient of variation (CV)196.2100451
Kurtosis200337.4843
Mean3693.181796
Median Absolute Deviation (MAD)22
Skewness415.9170335
Sum1532748002
Variance5.251022118 × 1011
MonotonicityNot monotonic
2023-09-28T14:58:00.056991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 21165
 
5.1%
75 14356
 
3.5%
82 12511
 
3.0%
76 12362
 
3.0%
80 11583
 
2.8%
84 11414
 
2.8%
83 10834
 
2.6%
72 10106
 
2.4%
78 9944
 
2.4%
87 9323
 
2.2%
Other values (14871) 291423
70.2%
ValueCountFrequency (%)
46 2383
0.6%
54 19
 
< 0.1%
55 96
 
< 0.1%
60 544
 
0.1%
62 77
 
< 0.1%
ValueCountFrequency (%)
383809522 1
< 0.1%
146054307 2
< 0.1%
111505374 2
< 0.1%
51428692 1
< 0.1%
12644847 2
< 0.1%

dst2src_first_seen_ms
Real number (ℝ)

Distinct352129
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.893050578 × 1011
Minimum0
Maximum1.493732961 × 1012
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:00.384277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13190931
median8531592
Q31.427221368 × 1012
95-th percentile1.493731324 × 1012
Maximum1.493732961 × 1012
Range1.493732961 × 1012
Interquartile range (IQR)1.427218177 × 1012

Descriptive statistics

Standard deviation6.53331114 × 1011
Coefficient of variation (CV)1.678198372
Kurtosis-0.8258136907
Mean3.893050578 × 1011
Median Absolute Deviation (MAD)5940580
Skewness1.082981429
Sum1.615697744 × 1017
Variance4.268415445 × 1023
MonotonicityNot monotonic
2023-09-28T14:58:00.648535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25599
 
6.2%
9849460 14
 
< 0.1%
1.493728184 × 101213
 
< 0.1%
1.493729117 × 101212
 
< 0.1%
1.49373113 × 101212
 
< 0.1%
1.493729386 × 101212
 
< 0.1%
1.493730924 × 101212
 
< 0.1%
1.493729255 × 101211
 
< 0.1%
756205 11
 
< 0.1%
10348770 11
 
< 0.1%
Other values (352119) 389314
93.8%
ValueCountFrequency (%)
0 25599
6.2%
12401 1
 
< 0.1%
13099 1
 
< 0.1%
13119 1
 
< 0.1%
13139 1
 
< 0.1%
ValueCountFrequency (%)
1.493732961 × 10121
< 0.1%
1.49373296 × 10122
< 0.1%
1.49373296 × 10121
< 0.1%
1.49373296 × 10121
< 0.1%
1.49373296 × 10121
< 0.1%

dst2src_last_seen_ms
Real number (ℝ)

Distinct359504
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8930508 × 1011
Minimum0
Maximum1.493732969 × 1012
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:00.942278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13212573
median8547060
Q31.427221368 × 1012
95-th percentile1.493731383 × 1012
Maximum1.493732969 × 1012
Range1.493732969 × 1012
Interquartile range (IQR)1.427218156 × 1012

Descriptive statistics

Standard deviation6.533311177 × 1011
Coefficient of variation (CV)1.678198285
Kurtosis-0.8258136925
Mean3.8930508 × 1011
Median Absolute Deviation (MAD)5936140
Skewness1.082981428
Sum1.615697836 × 1017
Variance4.268415494 × 1023
MonotonicityNot monotonic
2023-09-28T14:58:01.271662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25599
 
6.2%
1.493731028 × 101215
 
< 0.1%
1.493730472 × 101212
 
< 0.1%
1.3873157 × 101210
 
< 0.1%
1.493731296 × 101210
 
< 0.1%
1.493729304 × 10129
 
< 0.1%
13569002 9
 
< 0.1%
1.493730925 × 10129
 
< 0.1%
1.493728797 × 10129
 
< 0.1%
1.493731049 × 10129
 
< 0.1%
Other values (359494) 389330
93.8%
ValueCountFrequency (%)
0 25599
6.2%
13099 1
 
< 0.1%
13119 1
 
< 0.1%
13139 1
 
< 0.1%
13158 1
 
< 0.1%
ValueCountFrequency (%)
1.493732969 × 10121
< 0.1%
1.493732969 × 10121
< 0.1%
1.493732969 × 10122
< 0.1%
1.493732969 × 10121
< 0.1%
1.493732969 × 10121
< 0.1%

dst2src_duration_ms
Real number (ℝ)

Distinct57748
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22190.95598
Minimum0
Maximum1799978
Zeros249939
Zeros (%)60.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:01.601461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33864
95-th percentile124906
Maximum1799978
Range1799978
Interquartile range (IQR)3864

Descriptive statistics

Standard deviation102536.9137
Coefficient of variation (CV)4.620662302
Kurtosis202.1344272
Mean22190.95598
Median Absolute Deviation (MAD)0
Skewness12.73189013
Sum9209712740
Variance1.051381868 × 1010
MonotonicityNot monotonic
2023-09-28T14:58:01.896997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 249939
60.2%
1 5678
 
1.4%
2 1772
 
0.4%
3 1241
 
0.3%
4 873
 
0.2%
5 643
 
0.2%
6 571
 
0.1%
8 555
 
0.1%
9 552
 
0.1%
10 538
 
0.1%
Other values (57738) 152659
36.8%
ValueCountFrequency (%)
0 249939
60.2%
1 5678
 
1.4%
2 1772
 
0.4%
3 1241
 
0.3%
4 873
 
0.2%
ValueCountFrequency (%)
1799978 1
< 0.1%
1799966 1
< 0.1%
1799918 1
< 0.1%
1799917 1
< 0.1%
1799884 1
< 0.1%

dst2src_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct1477
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.44076083
Minimum0
Maximum238241
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:02.354419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q36
95-th percentile39
Maximum238241
Range238241
Interquartile range (IQR)5

Descriptive statistics

Standard deviation542.7947373
Coefficient of variation (CV)31.12219373
Kurtosis102190.9362
Mean17.44076083
Median Absolute Deviation (MAD)0
Skewness277.9255337
Sum7238282
Variance294626.1269
MonotonicityNot monotonic
2023-09-28T14:58:02.640306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 212785
51.3%
2 43630
 
10.5%
0 25599
 
6.2%
3 15555
 
3.7%
7 8649
 
2.1%
5 6716
 
1.6%
9 6230
 
1.5%
16 5627
 
1.4%
10 5343
 
1.3%
11 5127
 
1.2%
Other values (1467) 79760
 
19.2%
ValueCountFrequency (%)
0 25599
 
6.2%
1 212785
51.3%
2 43630
 
10.5%
3 15555
 
3.7%
4 4537
 
1.1%
ValueCountFrequency (%)
238241 1
< 0.1%
120378 1
< 0.1%
92794 2
< 0.1%
75240 1
< 0.1%
74533 2
< 0.1%

dst2src_bytes
Real number (ℝ)

SKEWED  ZEROS 

Distinct34871
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14905.52242
Minimum0
Maximum305372427
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:02.929673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1144
median265
Q3876
95-th percentile22324
Maximum305372427
Range305372427
Interquartile range (IQR)732

Descriptive statistics

Standard deviation613313.0763
Coefficient of variation (CV)41.14670113
Kurtosis155532.0012
Mean14905.52242
Median Absolute Deviation (MAD)164
Skewness345.0878528
Sum6186104821
Variance3.761529295 × 1011
MonotonicityNot monotonic
2023-09-28T14:58:03.194791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25599
 
6.2%
166 11851
 
2.9%
136 7990
 
1.9%
244 4304
 
1.0%
91 4195
 
1.0%
143 4009
 
1.0%
144 3796
 
0.9%
174 3670
 
0.9%
90 3410
 
0.8%
102 3282
 
0.8%
Other values (34861) 342915
82.6%
ValueCountFrequency (%)
0 25599
6.2%
54 389
 
0.1%
58 745
 
0.2%
62 1
 
< 0.1%
66 166
 
< 0.1%
ValueCountFrequency (%)
305372427 1
< 0.1%
108240611 2
< 0.1%
107191507 1
< 0.1%
57515689 1
< 0.1%
48622876 1
< 0.1%

bidirectional_min_ps
Real number (ℝ)

Distinct319
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.13669911
Minimum43
Maximum590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:03.527818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q166
median75
Q383
95-th percentile95
Maximum590
Range547
Interquartile range (IQR)17

Descriptive statistics

Standard deviation29.85102868
Coefficient of variation (CV)0.392071485
Kurtosis74.41179458
Mean76.13669911
Median Absolute Deviation (MAD)9
Skewness7.400061746
Sum31598329
Variance891.0839132
MonotonicityNot monotonic
2023-09-28T14:58:03.801395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 93819
22.6%
66 46729
 
11.3%
86 23099
 
5.6%
75 18149
 
4.4%
76 15457
 
3.7%
80 14646
 
3.5%
82 13854
 
3.3%
84 13133
 
3.2%
78 12596
 
3.0%
83 12449
 
3.0%
Other values (309) 151090
36.4%
ValueCountFrequency (%)
43 48
 
< 0.1%
46 2513
 
0.6%
54 93819
22.6%
55 96
 
< 0.1%
56 1
 
< 0.1%
ValueCountFrequency (%)
590 11
< 0.1%
553 6
< 0.1%
551 1
 
< 0.1%
549 4
 
< 0.1%
548 1
 
< 0.1%

bidirectional_mean_ps
Real number (ℝ)

Distinct82327
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.8174425
Minimum46
Maximum1350.138895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:04.078198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile68
Q1104
median150.5
Q3220.5
95-th percentile474.1428571
Maximum1350.138895
Range1304.138895
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation142.7237021
Coefficient of variation (CV)0.759906536
Kurtosis9.862492775
Mean187.8174425
Median Absolute Deviation (MAD)52.7
Skewness2.798722796
Sum77948182.8
Variance20370.05515
MonotonicityNot monotonic
2023-09-28T14:58:04.367584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 14973
 
3.6%
68 6466
 
1.6%
90 6226
 
1.5%
55.71428571 5543
 
1.3%
56.28571429 4870
 
1.2%
160 4325
 
1.0%
83 4219
 
1.0%
114 4062
 
1.0%
112.5 3814
 
0.9%
156.5 3641
 
0.9%
Other values (82317) 356882
86.0%
ValueCountFrequency (%)
46 2513
0.6%
54 75
 
< 0.1%
54.5 48
 
< 0.1%
55 96
 
< 0.1%
55.03581267 2
 
< 0.1%
ValueCountFrequency (%)
1350.138895 1
< 0.1%
1307.40428 1
< 0.1%
1305.295718 1
< 0.1%
1301.207532 1
< 0.1%
1298.695546 1
< 0.1%

bidirectional_stddev_ps
Real number (ℝ)

Distinct103447
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.0166362
Minimum0
Maximum728.1035406
Zeros29450
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:04.638693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136.76955262
median107.293709
Q3228.6511171
95-th percentile608.2789211
Maximum728.1035406
Range728.1035406
Interquartile range (IQR)191.8815644

Descriptive statistics

Standard deviation185.3973552
Coefficient of variation (CV)1.090465965
Kurtosis0.859615843
Mean170.0166362
Median Absolute Deviation (MAD)87.49471917
Skewness1.356995359
Sum70560474.38
Variance34372.17933
MonotonicityNot monotonic
2023-09-28T14:58:04.914831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29450
 
7.1%
11.3137085 26012
 
6.3%
43.13351365 7358
 
1.8%
19.79898987 7118
 
1.7%
3.346640106 6184
 
1.5%
3.14718317 5543
 
1.3%
98.28784258 5272
 
1.3%
187.383297 5029
 
1.2%
4.535573676 4870
 
1.2%
106.773124 4528
 
1.1%
Other values (103437) 313657
75.6%
ValueCountFrequency (%)
0 29450
7.1%
0.5006958946 40
 
< 0.1%
0.5007199428 2
 
< 0.1%
0.5007283325 2
 
< 0.1%
0.5007326011 1
 
< 0.1%
ValueCountFrequency (%)
728.1035406 1
< 0.1%
727.2549306 1
< 0.1%
723.849159 1
< 0.1%
723.6323307 1
< 0.1%
723.2549888 1
< 0.1%

bidirectional_max_ps
Real number (ℝ)

Distinct1456
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean491.7698213
Minimum46
Maximum1514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:05.182727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile74
Q1140
median251
Q3571
95-th percentile1514
Maximum1514
Range1468
Interquartile range (IQR)431

Descriptive statistics

Standard deviation513.3465323
Coefficient of variation (CV)1.043875631
Kurtosis-0.1609297518
Mean491.7698213
Median Absolute Deviation (MAD)150
Skewness1.227948908
Sum204094803
Variance263524.6622
MonotonicityNot monotonic
2023-09-28T14:58:05.431531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1474 41549
 
10.0%
1514 31393
 
7.6%
86 13059
 
3.1%
74 11268
 
2.7%
62 7230
 
1.7%
143 5692
 
1.4%
66 5500
 
1.3%
90 5016
 
1.2%
571 4708
 
1.1%
244 4553
 
1.1%
Other values (1446) 285053
68.7%
ValueCountFrequency (%)
46 2513
 
0.6%
54 75
 
< 0.1%
55 144
 
< 0.1%
60 616
 
0.1%
62 7230
1.7%
ValueCountFrequency (%)
1514 31393
7.6%
1513 9
 
< 0.1%
1512 15
 
< 0.1%
1511 12
 
< 0.1%
1510 118
 
< 0.1%

src2dst_min_ps
Real number (ℝ)

Distinct324
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.30905424
Minimum43
Maximum994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:05.706387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q166
median75
Q383
95-th percentile95
Maximum994
Range951
Interquartile range (IQR)17

Descriptive statistics

Standard deviation29.96695389
Coefficient of variation (CV)0.3927050884
Kurtosis78.43472117
Mean76.30905424
Median Absolute Deviation (MAD)9
Skewness7.553230456
Sum31669860
Variance898.0183255
MonotonicityNot monotonic
2023-09-28T14:58:05.965442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 89042
21.5%
66 50978
 
12.3%
86 23108
 
5.6%
75 18149
 
4.4%
76 15456
 
3.7%
80 14648
 
3.5%
82 13854
 
3.3%
84 13131
 
3.2%
78 12586
 
3.0%
83 12448
 
3.0%
Other values (314) 151621
36.5%
ValueCountFrequency (%)
43 48
 
< 0.1%
46 2513
 
0.6%
54 89042
21.5%
55 144
 
< 0.1%
58 2
 
< 0.1%
ValueCountFrequency (%)
994 1
 
< 0.1%
590 28
< 0.1%
553 6
 
< 0.1%
551 1
 
< 0.1%
549 4
 
< 0.1%

src2dst_mean_ps
Real number (ℝ)

Distinct52069
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.1498769
Minimum46
Maximum1496.22811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:06.218390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile67.6
Q176
median83
Q394.94736842
95-th percentile198
Maximum1496.22811
Range1450.22811
Interquartile range (IQR)18.94736842

Descriptive statistics

Standard deviation69.36079424
Coefficient of variation (CV)0.6857229721
Kurtosis79.50926796
Mean101.1498769
Median Absolute Deviation (MAD)8
Skewness6.959449006
Sum41979323.08
Variance4810.919778
MonotonicityNot monotonic
2023-09-28T14:58:06.505663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 23054
 
5.6%
75 17837
 
4.3%
76 15202
 
3.7%
80 14570
 
3.5%
82 13808
 
3.3%
84 13182
 
3.2%
78 12453
 
3.0%
83 12405
 
3.0%
72 12004
 
2.9%
77 11136
 
2.7%
Other values (52059) 269370
64.9%
ValueCountFrequency (%)
46 2513
0.6%
54 75
 
< 0.1%
54.03202149 1
 
< 0.1%
54.103558 1
 
< 0.1%
54.14763738 1
 
< 0.1%
ValueCountFrequency (%)
1496.22811 2
< 0.1%
1482.564644 1
< 0.1%
1480.155116 1
< 0.1%
1478.520593 1
< 0.1%
1476.822064 1
< 0.1%

src2dst_stddev_ps
Real number (ℝ)

Distinct76650
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.91977101
Minimum0
Maximum1023.890619
Zeros269545
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:06.797230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q368.72824331
95-th percentile281.2108738
Maximum1023.890619
Range1023.890619
Interquartile range (IQR)68.72824331

Descriptive statistics

Standard deviation111.4894548
Coefficient of variation (CV)2.147341035
Kurtosis9.847329173
Mean51.91977101
Median Absolute Deviation (MAD)0
Skewness2.968895274
Sum21547795.28
Variance12429.89853
MonotonicityNot monotonic
2023-09-28T14:58:07.063242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 269545
64.9%
4 8414
 
2.0%
4 5544
 
1.3%
6 4872
 
1.2%
68.72824331 2527
 
0.6%
4.618802154 1026
 
0.2%
82.36018456 954
 
0.2%
5.656854249 935
 
0.2%
179.0171081 717
 
0.2%
2.828427125 662
 
0.2%
Other values (76640) 119825
28.9%
ValueCountFrequency (%)
0 269545
64.9%
0.4472135955 1
 
< 0.1%
0.5 4
 
< 0.1%
0.5 2
 
< 0.1%
0.5477225575 1
 
< 0.1%
ValueCountFrequency (%)
1023.890619 1
< 0.1%
1018.233765 1
< 0.1%
795.3082421 1
< 0.1%
784.4513157 1
< 0.1%
757.9997361 1
< 0.1%

src2dst_max_ps
Real number (ℝ)

Distinct1443
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.3800747
Minimum46
Maximum1514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:07.331236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile69
Q176
median84
Q3259
95-th percentile1015
Maximum1514
Range1468
Interquartile range (IQR)183

Descriptive statistics

Standard deviation330.7033516
Coefficient of variation (CV)1.364399908
Kurtosis5.906996433
Mean242.3800747
Median Absolute Deviation (MAD)10
Skewness2.482926931
Sum100592821
Variance109364.7068
MonotonicityNot monotonic
2023-09-28T14:58:07.650795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 23339
 
5.6%
74 21863
 
5.3%
75 17817
 
4.3%
76 15204
 
3.7%
80 14515
 
3.5%
82 13938
 
3.4%
84 13327
 
3.2%
83 12601
 
3.0%
78 12451
 
3.0%
1514 12275
 
3.0%
Other values (1433) 257691
62.1%
ValueCountFrequency (%)
46 2513
 
0.6%
54 75
 
< 0.1%
55 144
 
< 0.1%
60 616
 
0.1%
62 7238
1.7%
ValueCountFrequency (%)
1514 12275
3.0%
1513 8
 
< 0.1%
1512 12
 
< 0.1%
1511 15
 
< 0.1%
1510 69
 
< 0.1%

dst2src_min_ps
Real number (ℝ)

Distinct499
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.1390725
Minimum0
Maximum1224
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:07.956796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154
median117
Q3220
95-th percentile393
Maximum1224
Range1224
Interquartile range (IQR)166

Descriptive statistics

Standard deviation116.8490987
Coefficient of variation (CV)0.7834908504
Kurtosis1.030879939
Mean149.1390725
Median Absolute Deviation (MAD)63
Skewness1.187207923
Sum61895847
Variance13653.71187
MonotonicityNot monotonic
2023-09-28T14:58:08.240380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 86151
 
20.8%
66 51715
 
12.5%
0 25599
 
6.2%
91 5212
 
1.3%
143 4604
 
1.1%
244 4581
 
1.1%
90 4401
 
1.1%
98 4177
 
1.0%
174 4067
 
1.0%
144 3991
 
1.0%
Other values (489) 220523
53.1%
ValueCountFrequency (%)
0 25599
 
6.2%
43 48
 
< 0.1%
54 86151
20.8%
56 1
 
< 0.1%
58 748
 
0.2%
ValueCountFrequency (%)
1224 1
 
< 0.1%
701 2
 
< 0.1%
683 1
 
< 0.1%
565 2
 
< 0.1%
554 93
< 0.1%

dst2src_mean_ps
Real number (ℝ)

Distinct64979
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.0715909
Minimum0
Maximum1512.885002
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:08.527210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1108.5
median193.5555556
Q3325.5
95-th percentile699.5555556
Maximum1512.885002
Range1512.885002
Interquartile range (IQR)217

Descriptive statistics

Standard deviation228.4080086
Coefficient of variation (CV)0.89546628
Kurtosis6.493311187
Mean255.0715909
Median Absolute Deviation (MAD)95.55555556
Skewness2.247686265
Sum105860066.7
Variance52170.21839
MonotonicityNot monotonic
2023-09-28T14:58:08.790164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25599
 
6.2%
55.33333333 11115
 
2.7%
68 7048
 
1.7%
244 4421
 
1.1%
90 4346
 
1.0%
91 4233
 
1.0%
98 4097
 
1.0%
143 4079
 
1.0%
144 3841
 
0.9%
174 3741
 
0.9%
Other values (64969) 342501
82.5%
ValueCountFrequency (%)
0 25599
6.2%
54 447
 
0.1%
54.02197802 2
 
< 0.1%
54.02209945 6
 
< 0.1%
54.05548426 1
 
< 0.1%
ValueCountFrequency (%)
1512.885002 1
< 0.1%
1509.840695 1
< 0.1%
1509.772182 1
< 0.1%
1509.61316 1
< 0.1%
1507.84294 1
< 0.1%

dst2src_stddev_ps
Real number (ℝ)

Distinct80605
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.2610067
Minimum0
Maximum1023.890619
Zeros245007
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:09.075157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3138.5929291
95-th percentile645.446822
Maximum1023.890619
Range1023.890619
Interquartile range (IQR)138.5929291

Descriptive statistics

Standard deviation220.1566962
Coefficient of variation (CV)1.771727931
Kurtosis0.8462345711
Mean124.2610067
Median Absolute Deviation (MAD)0
Skewness1.571530381
Sum51570927.26
Variance48468.9709
MonotonicityNot monotonic
2023-09-28T14:58:09.525058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245007
59.0%
2.309401077 11115
 
2.7%
2.828427125 7265
 
1.8%
8.485281374 6176
 
1.5%
657.8197394 2552
 
0.6%
522.3569661 1306
 
0.3%
66.46803743 1110
 
0.3%
5.656854249 1091
 
0.3%
72.12489168 889
 
0.2%
2.309401077 879
 
0.2%
Other values (80595) 137631
33.2%
ValueCountFrequency (%)
0 245007
59.0%
0.2964997267 2
 
< 0.1%
0.2973176585 6
 
< 0.1%
0.5 1
 
< 0.1%
0.5773502692 1
 
< 0.1%
ValueCountFrequency (%)
1023.890619 1
 
< 0.1%
833.7033845 1
 
< 0.1%
818.6851247 3
< 0.1%
813.2525233 1
 
< 0.1%
810.2320655 1
 
< 0.1%

dst2src_max_ps
Real number (ℝ)

Distinct1448
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.4691498
Minimum0
Maximum1514
Zeros25599
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:09.781545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1132
median239
Q3491
95-th percentile1514
Maximum1514
Range1514
Interquartile range (IQR)359

Descriptive statistics

Standard deviation505.6520685
Coefficient of variation (CV)1.105324958
Kurtosis0.1551889258
Mean457.4691498
Median Absolute Deviation (MAD)140
Skewness1.335945738
Sum189859304
Variance255684.0144
MonotonicityNot monotonic
2023-09-28T14:58:10.058565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1474 46936
 
11.3%
0 25599
 
6.2%
1514 21933
 
5.3%
58 12350
 
3.0%
70 9260
 
2.2%
244 4547
 
1.1%
90 4329
 
1.0%
91 4213
 
1.0%
143 4145
 
1.0%
98 4072
 
1.0%
Other values (1438) 277637
66.9%
ValueCountFrequency (%)
0 25599
6.2%
54 447
 
0.1%
58 12350
3.0%
60 3
 
< 0.1%
61 27
 
< 0.1%
ValueCountFrequency (%)
1514 21933
5.3%
1513 1
 
< 0.1%
1512 4
 
< 0.1%
1511 3
 
< 0.1%
1510 115
 
< 0.1%

bidirectional_min_piat_ms
Real number (ℝ)

Distinct2695
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.4903294
Minimum0
Maximum119989
Zeros168170
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:10.378194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q333
95-th percentile232
Maximum119989
Range119989
Interquartile range (IQR)33

Descriptive statistics

Standard deviation6987.268712
Coefficient of variation (CV)14.48167618
Kurtosis275.0538812
Mean482.4903294
Median Absolute Deviation (MAD)12
Skewness16.55470059
Sum200243619
Variance48821924.05
MonotonicityNot monotonic
2023-09-28T14:58:10.654659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 168170
40.5%
1 17716
 
4.3%
25 5751
 
1.4%
24 5708
 
1.4%
23 5625
 
1.4%
26 5602
 
1.3%
27 5510
 
1.3%
28 5421
 
1.3%
22 5302
 
1.3%
29 5257
 
1.3%
Other values (2685) 184959
44.6%
ValueCountFrequency (%)
0 168170
40.5%
1 17716
 
4.3%
2 3427
 
0.8%
3 1123
 
0.3%
4 689
 
0.2%
ValueCountFrequency (%)
119989 2
< 0.1%
119988 2
< 0.1%
119987 1
 
< 0.1%
119985 1
 
< 0.1%
119980 3
< 0.1%
Distinct122073
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1129.786211
Minimum0
Maximum119989
Zeros19204
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:10.920118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q122
median45
Q3373.4
95-th percentile3411.705882
Maximum119989
Range119989
Interquartile range (IQR)351.4

Descriptive statistics

Standard deviation7441.111012
Coefficient of variation (CV)6.586300081
Kurtosis210.8938328
Mean1129.786211
Median Absolute Deviation (MAD)37
Skewness13.94412236
Sum468885003
Variance55370133.09
MonotonicityNot monotonic
2023-09-28T14:58:11.185572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19204
 
4.6%
25 5724
 
1.4%
24 5659
 
1.4%
23 5570
 
1.3%
26 5541
 
1.3%
27 5433
 
1.3%
28 5351
 
1.3%
22 5260
 
1.3%
29 5197
 
1.3%
21 5185
 
1.2%
Other values (122063) 346897
83.6%
ValueCountFrequency (%)
0 19204
4.6%
0.2 2
 
< 0.1%
0.25 49
 
< 0.1%
0.3333333333 113
 
< 0.1%
0.4 4
 
< 0.1%
ValueCountFrequency (%)
119989 2
< 0.1%
119988 2
< 0.1%
119987 1
 
< 0.1%
119985 1
 
< 0.1%
119980 3
< 0.1%
Distinct154492
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1304.297185
Minimum0
Maximum81606.48651
Zeros228976
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:11.509043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3666.0972629
95-th percentile4803.280041
Maximum81606.48651
Range81606.48651
Interquartile range (IQR)666.0972629

Descriptive statistics

Standard deviation4586.811018
Coefficient of variation (CV)3.51669165
Kurtosis86.50036627
Mean1304.297185
Median Absolute Deviation (MAD)0
Skewness8.220291543
Sum541310721.8
Variance21038835.32
MonotonicityNot monotonic
2023-09-28T14:58:11.805622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228976
55.2%
1.527525232 709
 
0.2%
1 630
 
0.2%
1.154700538 590
 
0.1%
1.732050808 512
 
0.1%
2.081665999 407
 
0.1%
2.309401077 342
 
0.1%
2.886751346 265
 
0.1%
2.645751311 264
 
0.1%
3.464101615 252
 
0.1%
Other values (154482) 182074
43.9%
ValueCountFrequency (%)
0 228976
55.2%
0.3333333333 3
 
< 0.1%
0.4264014327 1
 
< 0.1%
0.4409585518 5
 
< 0.1%
0.4472135955 2
 
< 0.1%
ValueCountFrequency (%)
81606.48651 1
< 0.1%
80981.40412 1
< 0.1%
79552.34131 1
< 0.1%
79453.34636 1
< 0.1%
78082.97342 1
< 0.1%

bidirectional_max_piat_ms
Real number (ℝ)

Distinct24595
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4472.463598
Minimum0
Maximum119996
Zeros19204
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:12.082754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median57
Q32999
95-th percentile10318
Maximum119996
Range119996
Interquartile range (IQR)2975

Descriptive statistics

Standard deviation13833.95542
Coefficient of variation (CV)3.093139858
Kurtosis31.3416195
Mean4472.463598
Median Absolute Deviation (MAD)54
Skewness5.291503917
Sum1856166315
Variance191378322.6
MonotonicityNot monotonic
2023-09-28T14:58:12.393275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19204
 
4.6%
25 5874
 
1.4%
24 5839
 
1.4%
23 5796
 
1.4%
26 5770
 
1.4%
27 5626
 
1.4%
28 5558
 
1.3%
22 5495
 
1.3%
21 5432
 
1.3%
29 5378
 
1.3%
Other values (24585) 345049
83.1%
ValueCountFrequency (%)
0 19204
4.6%
1 1041
 
0.3%
2 1755
 
0.4%
3 1993
 
0.5%
4 1592
 
0.4%
ValueCountFrequency (%)
119996 1
< 0.1%
119989 2
< 0.1%
119988 2
< 0.1%
119987 1
< 0.1%
119985 1
< 0.1%

src2dst_min_piat_ms
Real number (ℝ)

Distinct7469
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1029.298233
Minimum0
Maximum120189
Zeros311178
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:12.685683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile242
Maximum120189
Range120189
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9427.562009
Coefficient of variation (CV)9.159213244
Kurtosis119.7918781
Mean1029.298233
Median Absolute Deviation (MAD)0
Skewness10.72197161
Sum427180382
Variance88878925.43
MonotonicityNot monotonic
2023-09-28T14:58:12.956535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311178
75.0%
1 23397
 
5.6%
2 9821
 
2.4%
3 6147
 
1.5%
4 3607
 
0.9%
5 2299
 
0.6%
10 1933
 
0.5%
9 1888
 
0.5%
6 1797
 
0.4%
8 1773
 
0.4%
Other values (7459) 51181
 
12.3%
ValueCountFrequency (%)
0 311178
75.0%
1 23397
 
5.6%
2 9821
 
2.4%
3 6147
 
1.5%
4 3607
 
0.9%
ValueCountFrequency (%)
120189 1
< 0.1%
120130 1
< 0.1%
120027 1
< 0.1%
119989 2
< 0.1%
119988 2
< 0.1%

src2dst_mean_piat_ms
Real number (ℝ)

Distinct107827
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1926.406054
Minimum0
Maximum120189
Zeros251285
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:13.219842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3691.3636364
95-th percentile6995.772727
Maximum120189
Range120189
Interquartile range (IQR)691.3636364

Descriptive statistics

Standard deviation9740.923326
Coefficient of variation (CV)5.056526534
Kurtosis102.4273566
Mean1926.406054
Median Absolute Deviation (MAD)0
Skewness9.683272011
Sum799498966.8
Variance94885587.24
MonotonicityNot monotonic
2023-09-28T14:58:13.516220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 251285
60.5%
1 1366
 
0.3%
103 233
 
0.1%
4 211
 
0.1%
104 208
 
0.1%
206 208
 
0.1%
2 207
 
< 0.1%
6 194
 
< 0.1%
5 192
 
< 0.1%
3 189
 
< 0.1%
Other values (107817) 160728
38.7%
ValueCountFrequency (%)
0 251285
60.5%
0.25 49
 
< 0.1%
0.5 19
 
< 0.1%
0.75 1
 
< 0.1%
1 1366
 
0.3%
ValueCountFrequency (%)
120189 1
< 0.1%
120130 1
< 0.1%
120027 1
< 0.1%
119989 2
< 0.1%
119988 2
< 0.1%

src2dst_stddev_piat_ms
Real number (ℝ)

Distinct137407
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1274.333038
Minimum0
Maximum84221.36739
Zeros268171
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:13.779495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3614.591192
95-th percentile4886.216924
Maximum84221.36739
Range84221.36739
Interquartile range (IQR)614.591192

Descriptive statistics

Standard deviation3846.211516
Coefficient of variation (CV)3.018215334
Kurtosis82.63224874
Mean1274.333038
Median Absolute Deviation (MAD)0
Skewness7.329107015
Sum528874971.8
Variance14793343.03
MonotonicityNot monotonic
2023-09-28T14:58:14.067037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 268171
64.6%
0.7071067812 528
 
0.1%
1.414213562 364
 
0.1%
2.121320344 186
 
< 0.1%
2.828427125 144
 
< 0.1%
4.242640687 113
 
< 0.1%
3.535533906 102
 
< 0.1%
4.949747468 101
 
< 0.1%
5.656854249 91
 
< 0.1%
1 87
 
< 0.1%
Other values (137397) 145134
35.0%
ValueCountFrequency (%)
0 268171
64.6%
0.3333333333 1
 
< 0.1%
0.4472135955 2
 
< 0.1%
0.5 12
 
< 0.1%
0.5 85
 
< 0.1%
ValueCountFrequency (%)
84221.36739 1
< 0.1%
84151.36382 1
< 0.1%
84030.44856 1
< 0.1%
83972.4658 1
< 0.1%
83682.55202 1
< 0.1%

src2dst_max_piat_ms
Real number (ℝ)

Distinct25034
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4462.239349
Minimum0
Maximum131048
Zeros251285
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:14.318656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33001
95-th percentile10619
Maximum131048
Range131048
Interquartile range (IQR)3001

Descriptive statistics

Standard deviation13874.49056
Coefficient of variation (CV)3.109311149
Kurtosis31.09518112
Mean4462.239349
Median Absolute Deviation (MAD)0
Skewness5.265618936
Sum1851923037
Variance192501488.3
MonotonicityNot monotonic
2023-09-28T14:58:14.597866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 251285
60.5%
1 1420
 
0.3%
10048 791
 
0.2%
10080 677
 
0.2%
10101 648
 
0.2%
10112 635
 
0.2%
10100 631
 
0.2%
10098 607
 
0.1%
10095 601
 
0.1%
10094 595
 
0.1%
Other values (25024) 157131
37.9%
ValueCountFrequency (%)
0 251285
60.5%
1 1420
 
0.3%
2 160
 
< 0.1%
3 131
 
< 0.1%
4 206
 
< 0.1%
ValueCountFrequency (%)
131048 1
< 0.1%
121741 1
< 0.1%
120824 1
< 0.1%
120189 1
< 0.1%
120130 1
< 0.1%

dst2src_min_piat_ms
Real number (ℝ)

Distinct8324
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean657.066334
Minimum0
Maximum591145
Zeros344241
Zeros (%)82.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:14.884092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile170
Maximum591145
Range591145
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6723.128495
Coefficient of variation (CV)10.23203921
Kurtosis516.6210775
Mean657.066334
Median Absolute Deviation (MAD)0
Skewness16.28191204
Sum272696327
Variance45200456.77
MonotonicityNot monotonic
2023-09-28T14:58:15.137970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 344241
82.9%
1 10411
 
2.5%
2 3992
 
1.0%
3 3680
 
0.9%
4 2952
 
0.7%
5 2218
 
0.5%
6 1992
 
0.5%
7 1786
 
0.4%
8 1631
 
0.4%
9 1520
 
0.4%
Other values (8314) 40598
 
9.8%
ValueCountFrequency (%)
0 344241
82.9%
1 10411
 
2.5%
2 3992
 
1.0%
3 3680
 
0.9%
4 2952
 
0.7%
ValueCountFrequency (%)
591145 1
< 0.1%
590885 1
< 0.1%
472686 1
< 0.1%
236338 1
< 0.1%
197140 1
< 0.1%

dst2src_mean_piat_ms
Real number (ℝ)

Distinct103937
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1481.379431
Minimum0
Maximum591145
Zeros249939
Zeros (%)60.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:15.409377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3464.7142857
95-th percentile6473
Maximum591145
Range591145
Interquartile range (IQR)464.7142857

Descriptive statistics

Standard deviation7482.178018
Coefficient of variation (CV)5.050818084
Kurtosis546.8389528
Mean1481.379431
Median Absolute Deviation (MAD)0
Skewness16.18515587
Sum614803572.8
Variance55982987.89
MonotonicityNot monotonic
2023-09-28T14:58:15.658339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 249939
60.2%
1 5677
 
1.4%
2 1860
 
0.4%
3 1348
 
0.3%
4 1005
 
0.2%
5 784
 
0.2%
6 694
 
0.2%
7 575
 
0.1%
8 567
 
0.1%
9 553
 
0.1%
Other values (103927) 152019
36.6%
ValueCountFrequency (%)
0 249939
60.2%
0.3333333333 1
 
< 0.1%
0.375 1
 
< 0.1%
0.5 4
 
< 0.1%
0.9601226994 1
 
< 0.1%
ValueCountFrequency (%)
591145 1
< 0.1%
590885 1
< 0.1%
499291.6667 1
< 0.1%
472691.3333 1
< 0.1%
472686 1
< 0.1%

dst2src_stddev_piat_ms
Real number (ℝ)

SKEWED  ZEROS 

Distinct121043
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1176.937925
Minimum0
Maximum484430.9371
Zeros282181
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:15.956043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3381.1358026
95-th percentile4894.806323
Maximum484430.9371
Range484430.9371
Interquartile range (IQR)381.1358026

Descriptive statistics

Standard deviation4026.783422
Coefficient of variation (CV)3.421406804
Kurtosis1303.842615
Mean1176.937925
Median Absolute Deviation (MAD)0
Skewness20.81678377
Sum488453954.4
Variance16214984.73
MonotonicityNot monotonic
2023-09-28T14:58:16.269119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282181
68.0%
0.7071067812 308
 
0.1%
1.414213562 183
 
< 0.1%
2.121320344 101
 
< 0.1%
2.828427125 79
 
< 0.1%
4.242640687 75
 
< 0.1%
5.656854249 74
 
< 0.1%
3.535533906 68
 
< 0.1%
1.5 58
 
< 0.1%
4.949747468 55
 
< 0.1%
Other values (121033) 131839
31.8%
ValueCountFrequency (%)
0 282181
68.0%
0.3333333333 1
 
< 0.1%
0.4409585518 1
 
< 0.1%
0.4472135955 1
 
< 0.1%
0.5 11
 
< 0.1%
ValueCountFrequency (%)
484430.9371 1
< 0.1%
371016.5817 1
< 0.1%
306327.4519 1
< 0.1%
297417.4973 1
< 0.1%
279738.4932 1
< 0.1%

dst2src_max_piat_ms
Real number (ℝ)

Distinct23523
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3943.734864
Minimum0
Maximum985470
Zeros249939
Zeros (%)60.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:16.550342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32224
95-th percentile10320
Maximum985470
Range985470
Interquartile range (IQR)2224

Descriptive statistics

Standard deviation13024.24656
Coefficient of variation (CV)3.302515765
Kurtosis414.2255468
Mean3943.734864
Median Absolute Deviation (MAD)0
Skewness11.63529811
Sum1636732787
Variance169630998.5
MonotonicityNot monotonic
2023-09-28T14:58:16.830663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 249939
60.2%
1 5683
 
1.4%
2 1770
 
0.4%
3 1313
 
0.3%
4 1110
 
0.3%
5 854
 
0.2%
6 836
 
0.2%
8 718
 
0.2%
7 715
 
0.2%
10100 715
 
0.2%
Other values (23513) 151368
36.5%
ValueCountFrequency (%)
0 249939
60.2%
1 5683
 
1.4%
2 1770
 
0.4%
3 1313
 
0.3%
4 1110
 
0.3%
ValueCountFrequency (%)
985470 1
< 0.1%
866811 1
< 0.1%
749271 1
< 0.1%
748722 1
< 0.1%
748452 1
< 0.1%

bidirectional_syn_packets
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6807775992
Minimum0
Maximum9
Zeros276440
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:17.050136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9811511389
Coefficient of variation (CV)1.441221245
Kurtosis-0.3500607717
Mean0.6807775992
Median Absolute Deviation (MAD)0
Skewness0.9087426198
Sum282537
Variance0.9626575574
MonotonicityNot monotonic
2023-09-28T14:58:17.230197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 276440
66.6%
2 129653
31.2%
3 5382
 
1.3%
1 2572
 
0.6%
4 710
 
0.2%
7 102
 
< 0.1%
5 78
 
< 0.1%
6 52
 
< 0.1%
8 31
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 276440
66.6%
1 2572
 
0.6%
2 129653
31.2%
3 5382
 
1.3%
4 710
 
0.2%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 31
 
< 0.1%
7 102
< 0.1%
6 52
< 0.1%
5 78
< 0.1%

bidirectional_cwr_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:17.429873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:17.604622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

bidirectional_ece_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:17.782268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:17.945498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

bidirectional_urg_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:18.106422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:18.302339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

bidirectional_ack_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct1767
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.84922691
Minimum0
Maximum392213
Zeros274857
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:18.568608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile69
Maximum392213
Range392213
Interquartile range (IQR)12

Descriptive statistics

Standard deviation882.3401227
Coefficient of variation (CV)34.13410103
Kurtosis120457.6446
Mean25.84922691
Median Absolute Deviation (MAD)0
Skewness313.4888654
Sum10727972
Variance778524.0921
MonotonicityNot monotonic
2023-09-28T14:58:18.839026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 274857
66.2%
6 12637
 
3.0%
5 7775
 
1.9%
13 7066
 
1.7%
32 4191
 
1.0%
9 4055
 
1.0%
20 3640
 
0.9%
11 3451
 
0.8%
21 3306
 
0.8%
15 3223
 
0.8%
Other values (1757) 90820
 
21.9%
ValueCountFrequency (%)
0 274857
66.2%
1 1380
 
0.3%
2 183
 
< 0.1%
3 1152
 
0.3%
4 1142
 
0.3%
ValueCountFrequency (%)
392213 1
< 0.1%
275465 1
< 0.1%
124890 2
< 0.1%
110611 2
< 0.1%
89524 1
< 0.1%

bidirectional_psh_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct708
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.444473894
Minimum0
Maximum34903
Zeros298760
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:19.386465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile17
Maximum34903
Range34903
Interquartile range (IQR)2

Descriptive statistics

Standard deviation94.27165291
Coefficient of variation (CV)17.31510789
Kurtosis79218.71475
Mean5.444473894
Median Absolute Deviation (MAD)0
Skewness243.649542
Sum2259571
Variance8887.144542
MonotonicityNot monotonic
2023-09-28T14:58:19.663175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 298760
72.0%
2 13232
 
3.2%
3 11267
 
2.7%
6 10355
 
2.5%
7 10054
 
2.4%
5 9121
 
2.2%
4 8058
 
1.9%
8 7959
 
1.9%
9 5993
 
1.4%
10 4724
 
1.1%
Other values (698) 35498
 
8.6%
ValueCountFrequency (%)
0 298760
72.0%
1 1206
 
0.3%
2 13232
 
3.2%
3 11267
 
2.7%
4 8058
 
1.9%
ValueCountFrequency (%)
34903 1
< 0.1%
31175 1
< 0.1%
18767 1
< 0.1%
13249 1
< 0.1%
8200 1
< 0.1%

bidirectional_rst_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1742176902
Minimum0
Maximum259
Zeros385722
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:19.929232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum259
Range259
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.012855734
Coefficient of variation (CV)5.813736443
Kurtosis11725.55805
Mean0.1742176902
Median Absolute Deviation (MAD)0
Skewness63.0001639
Sum72304
Variance1.025876739
MonotonicityNot monotonic
2023-09-28T14:58:20.195565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 385722
92.9%
3 14022
 
3.4%
1 10314
 
2.5%
2 2902
 
0.7%
4 1035
 
0.2%
5 531
 
0.1%
9 87
 
< 0.1%
7 77
 
< 0.1%
6 69
 
< 0.1%
10 65
 
< 0.1%
Other values (51) 197
 
< 0.1%
ValueCountFrequency (%)
0 385722
92.9%
1 10314
 
2.5%
2 2902
 
0.7%
3 14022
 
3.4%
4 1035
 
0.2%
ValueCountFrequency (%)
259 1
< 0.1%
108 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%
89 1
< 0.1%

bidirectional_fin_packets
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6517597905
Minimum0
Maximum11
Zeros280364
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:20.404393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9780602332
Coefficient of variation (CV)1.500645249
Kurtosis1.189834392
Mean0.6517597905
Median Absolute Deviation (MAD)0
Skewness1.156335618
Sum270494
Variance0.9566018198
MonotonicityNot monotonic
2023-09-28T14:58:20.589965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 280364
67.6%
2 120380
29.0%
1 8251
 
2.0%
3 3992
 
1.0%
4 1340
 
0.3%
5 404
 
0.1%
6 119
 
< 0.1%
9 70
 
< 0.1%
8 66
 
< 0.1%
7 32
 
< 0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
0 280364
67.6%
1 8251
 
2.0%
2 120380
29.0%
3 3992
 
1.0%
4 1340
 
0.3%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 70
< 0.1%
8 66
< 0.1%
7 32
< 0.1%

src2dst_syn_packets
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3484305613
Minimum0
Maximum7
Zeros276441
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:20.803156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5207858425
Coefficient of variation (CV)1.494661778
Kurtosis8.991889317
Mean0.3484305613
Median Absolute Deviation (MAD)0
Skewness1.69982375
Sum144606
Variance0.2712178937
MonotonicityNot monotonic
2023-09-28T14:58:21.001102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 276441
66.6%
1 134216
32.3%
2 3390
 
0.8%
3 731
 
0.2%
7 103
 
< 0.1%
5 56
 
< 0.1%
4 44
 
< 0.1%
6 40
 
< 0.1%
ValueCountFrequency (%)
0 276441
66.6%
1 134216
32.3%
2 3390
 
0.8%
3 731
 
0.2%
4 44
 
< 0.1%
ValueCountFrequency (%)
7 103
 
< 0.1%
6 40
 
< 0.1%
5 56
 
< 0.1%
4 44
 
< 0.1%
3 731
0.2%

src2dst_cwr_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:21.192956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:21.350853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

src2dst_ece_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:21.530712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:21.701066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

src2dst_urg_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:21.917193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:22.072707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

src2dst_ack_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct834
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.652044113
Minimum0
Maximum271835
Zeros276261
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:22.319845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile29
Maximum271835
Range271835
Interquartile range (IQR)6

Descriptive statistics

Standard deviation492.7118077
Coefficient of variation (CV)51.04740529
Kurtosis230988.9829
Mean9.652044113
Median Absolute Deviation (MAD)0
Skewness446.7811648
Sum4005801
Variance242764.9255
MonotonicityNot monotonic
2023-09-28T14:58:22.596110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 276261
66.6%
3 20395
 
4.9%
6 10724
 
2.6%
9 9022
 
2.2%
16 7408
 
1.8%
7 6572
 
1.6%
8 6200
 
1.5%
10 5884
 
1.4%
5 4886
 
1.2%
11 4843
 
1.2%
Other values (824) 62826
 
15.1%
ValueCountFrequency (%)
0 276261
66.6%
1 301
 
0.1%
2 2740
 
0.7%
3 20395
 
4.9%
4 3652
 
0.9%
ValueCountFrequency (%)
271835 1
< 0.1%
97615 2
< 0.1%
42452 1
< 0.1%
37224 1
< 0.1%
36078 2
< 0.1%

src2dst_psh_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct277
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.624621405
Minimum0
Maximum18766
Zeros299459
Zeros (%)72.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:22.886282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum18766
Range18766
Interquartile range (IQR)1

Descriptive statistics

Standard deviation41.68541909
Coefficient of variation (CV)25.65854356
Kurtosis125754.8928
Mean1.624621405
Median Absolute Deviation (MAD)0
Skewness317.5344349
Sum674252
Variance1737.674165
MonotonicityNot monotonic
2023-09-28T14:58:23.155035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 299459
72.2%
1 32692
 
7.9%
3 28160
 
6.8%
4 16607
 
4.0%
2 9690
 
2.3%
5 6665
 
1.6%
6 4552
 
1.1%
7 3270
 
0.8%
9 1906
 
0.5%
8 1836
 
0.4%
Other values (267) 10184
 
2.5%
ValueCountFrequency (%)
0 299459
72.2%
1 32692
 
7.9%
2 9690
 
2.3%
3 28160
 
6.8%
4 16607
 
4.0%
ValueCountFrequency (%)
18766 1
< 0.1%
13248 1
< 0.1%
6653 1
< 0.1%
3861 1
< 0.1%
3363 1
< 0.1%

src2dst_rst_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1277959429
Minimum0
Maximum259
Zeros390954
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:23.424133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum259
Range259
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8504307826
Coefficient of variation (CV)6.654599227
Kurtosis22013.78597
Mean0.1277959429
Median Absolute Deviation (MAD)0
Skewness88.53294395
Sum53038
Variance0.723232516
MonotonicityNot monotonic
2023-09-28T14:58:23.697421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 390954
94.2%
2 10094
 
2.4%
1 7464
 
1.8%
3 4954
 
1.2%
4 902
 
0.2%
5 261
 
0.1%
6 77
 
< 0.1%
10 68
 
< 0.1%
9 53
 
< 0.1%
7 34
 
< 0.1%
Other values (44) 160
 
< 0.1%
ValueCountFrequency (%)
0 390954
94.2%
1 7464
 
1.8%
2 10094
 
2.4%
3 4954
 
1.2%
4 902
 
0.2%
ValueCountFrequency (%)
259 1
< 0.1%
108 1
< 0.1%
70 1
< 0.1%
55 1
< 0.1%
52 1
< 0.1%

src2dst_fin_packets
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3209235195
Minimum0
Maximum10
Zeros283006
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:23.964722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4780976986
Coefficient of variation (CV)1.489755875
Kurtosis6.333653883
Mean0.3209235195
Median Absolute Deviation (MAD)0
Skewness1.262598266
Sum133190
Variance0.2285774094
MonotonicityNot monotonic
2023-09-28T14:58:24.190670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 283006
68.2%
1 131189
31.6%
2 717
 
0.2%
3 46
 
< 0.1%
8 25
 
< 0.1%
4 12
 
< 0.1%
9 9
 
< 0.1%
5 9
 
< 0.1%
6 4
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
0 283006
68.2%
1 131189
31.6%
2 717
 
0.2%
3 46
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 9
 
< 0.1%
8 25
< 0.1%
7 3
 
< 0.1%
6 4
 
< 0.1%

dst2src_syn_packets
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3323470379
Minimum0
Maximum6
Zeros279978
Zeros (%)67.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:24.370881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.486663843
Coefficient of variation (CV)1.464324298
Kurtosis-0.3644946867
Mean0.3323470379
Median Absolute Deviation (MAD)0
Skewness0.9357547299
Sum137931
Variance0.2368416961
MonotonicityNot monotonic
2023-09-28T14:58:24.567891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 279978
67.5%
1 132349
31.9%
2 2513
 
0.6%
3 173
 
< 0.1%
4 5
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 279978
67.5%
1 132349
31.9%
2 2513
 
0.6%
3 173
 
< 0.1%
4 5
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 1
 
< 0.1%
4 5
 
< 0.1%
3 173
 
< 0.1%
2 2513
0.6%

dst2src_cwr_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:24.777476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:24.957712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

dst2src_ece_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:25.137215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:25.307660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

dst2src_urg_packets
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros415021
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:25.508328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-09-28T14:58:25.667285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%
ValueCountFrequency (%)
0 415021
100.0%

dst2src_ack_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct1478
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.19718279
Minimum0
Maximum238241
Zeros275043
Zeros (%)66.3%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:25.920063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile39
Maximum238241
Range238241
Interquartile range (IQR)6

Descriptive statistics

Standard deviation503.1436424
Coefficient of variation (CV)31.06365155
Kurtosis132849.166
Mean16.19718279
Median Absolute Deviation (MAD)0
Skewness318.740545
Sum6722171
Variance253153.5249
MonotonicityNot monotonic
2023-09-28T14:58:26.196331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 275043
66.3%
3 14019
 
3.4%
2 8593
 
2.1%
7 8502
 
2.0%
5 6245
 
1.5%
9 6171
 
1.5%
16 5637
 
1.4%
11 5066
 
1.2%
10 5024
 
1.2%
12 4745
 
1.1%
Other values (1468) 75976
 
18.3%
ValueCountFrequency (%)
0 275043
66.3%
1 2445
 
0.6%
2 8593
 
2.1%
3 14019
 
3.4%
4 3575
 
0.9%
ValueCountFrequency (%)
238241 1
< 0.1%
120378 1
< 0.1%
75240 1
< 0.1%
74533 2
< 0.1%
39095 1
< 0.1%

dst2src_psh_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct615
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.819852489
Minimum0
Maximum34899
Zeros300069
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:26.464045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum34899
Range34899
Interquartile range (IQR)1

Descriptive statistics

Standard deviation77.46304381
Coefficient of variation (CV)20.2790668
Kurtosis124226.4163
Mean3.819852489
Median Absolute Deviation (MAD)0
Skewness308.2854443
Sum1585319
Variance6000.523156
MonotonicityNot monotonic
2023-09-28T14:58:26.745496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 300069
72.3%
2 21743
 
5.2%
1 16812
 
4.1%
3 14755
 
3.6%
4 13505
 
3.3%
5 9339
 
2.3%
6 5853
 
1.4%
7 3729
 
0.9%
8 2832
 
0.7%
9 2334
 
0.6%
Other values (605) 24050
 
5.8%
ValueCountFrequency (%)
0 300069
72.3%
1 16812
 
4.1%
2 21743
 
5.2%
3 14755
 
3.6%
4 13505
 
3.3%
ValueCountFrequency (%)
34899 1
< 0.1%
24522 1
< 0.1%
8129 1
< 0.1%
7494 1
< 0.1%
5778 1
< 0.1%

dst2src_rst_packets
Real number (ℝ)

SKEWED  ZEROS 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04642174733
Minimum0
Maximum46
Zeros400230
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:27.037340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46
Range46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3361847535
Coefficient of variation (CV)7.24196681
Kurtosis4206.313014
Mean0.04642174733
Median Absolute Deviation (MAD)0
Skewness41.7481753
Sum19266
Variance0.1130201885
MonotonicityNot monotonic
2023-09-28T14:58:27.324783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 400230
96.4%
1 12020
 
2.9%
2 1987
 
0.5%
3 541
 
0.1%
4 145
 
< 0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%
7 6
 
< 0.1%
9 6
 
< 0.1%
17 4
 
< 0.1%
Other values (20) 32
 
< 0.1%
ValueCountFrequency (%)
0 400230
96.4%
1 12020
 
2.9%
2 1987
 
0.5%
3 541
 
0.1%
4 145
 
< 0.1%
ValueCountFrequency (%)
46 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
42 1
< 0.1%
36 1
< 0.1%

dst2src_fin_packets
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3308362709
Minimum0
Maximum8
Zeros286835
Zeros (%)69.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:27.544209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5398059197
Coefficient of variation (CV)1.631640685
Kurtosis13.32632888
Mean0.3308362709
Median Absolute Deviation (MAD)0
Skewness2.24143512
Sum137304
Variance0.291390431
MonotonicityNot monotonic
2023-09-28T14:58:27.771367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 286835
69.1%
1 122264
29.5%
2 3899
 
0.9%
3 1370
 
0.3%
4 418
 
0.1%
5 117
 
< 0.1%
8 68
 
< 0.1%
7 31
 
< 0.1%
6 19
 
< 0.1%
ValueCountFrequency (%)
0 286835
69.1%
1 122264
29.5%
2 3899
 
0.9%
3 1370
 
0.3%
4 418
 
0.1%
ValueCountFrequency (%)
8 68
 
< 0.1%
7 31
 
< 0.1%
6 19
 
< 0.1%
5 117
 
< 0.1%
4 418
0.1%
Distinct212
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:28.167069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length3
Mean length5.632127049
Min length3

Characters and Unicode

Total characters2337451
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowSSDP
2nd rowMDNS
3rd rowMDNS
4th rowDNS
5th rowDNS.Google
ValueCountFrequency (%)
dns 156853
37.8%
http 61773
 
14.9%
tls 51641
 
12.4%
dns.google 33234
 
8.0%
dns.facebook 18215
 
4.4%
dns.amazonaws 15063
 
3.6%
icmpv6 12860
 
3.1%
tls.google 4794
 
1.2%
http.ocsp 4078
 
1.0%
dns.twitter 3772
 
0.9%
Other values (202) 52738
 
12.7%
2023-09-28T14:58:29.038194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 347631
14.9%
N 251533
 
10.8%
D 249016
 
10.7%
T 225992
 
9.7%
o 169401
 
7.2%
. 113193
 
4.8%
P 103651
 
4.4%
e 91053
 
3.9%
H 71843
 
3.1%
L 69152
 
3.0%
Other values (46) 644986
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1544226
66.1%
Lowercase Letter 664630
28.4%
Other Punctuation 113193
 
4.8%
Decimal Number 14948
 
0.6%
Open Punctuation 155
 
< 0.1%
Close Punctuation 155
 
< 0.1%
Connector Punctuation 142
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 169401
25.5%
e 91053
13.7%
l 49580
 
7.5%
a 49268
 
7.4%
g 45507
 
6.8%
n 31622
 
4.8%
c 27886
 
4.2%
b 24682
 
3.7%
t 24273
 
3.7%
i 22610
 
3.4%
Other values (14) 128748
19.4%
Uppercase Letter
ValueCountFrequency (%)
S 347631
22.5%
N 251533
16.3%
D 249016
16.1%
T 225992
14.6%
P 103651
 
6.7%
H 71843
 
4.7%
L 69152
 
4.5%
G 48649
 
3.2%
A 40424
 
2.6%
C 23705
 
1.5%
Other values (12) 112630
 
7.3%
Decimal Number
ValueCountFrequency (%)
6 13401
89.7%
2 927
 
6.2%
3 244
 
1.6%
5 244
 
1.6%
1 132
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 113193
100.0%
Open Punctuation
ValueCountFrequency (%)
( 155
100.0%
Close Punctuation
ValueCountFrequency (%)
) 155
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2208856
94.5%
Common 128595
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 347631
15.7%
N 251533
11.4%
D 249016
11.3%
T 225992
 
10.2%
o 169401
 
7.7%
P 103651
 
4.7%
e 91053
 
4.1%
H 71843
 
3.3%
L 69152
 
3.1%
l 49580
 
2.2%
Other values (36) 580004
26.3%
Common
ValueCountFrequency (%)
. 113193
88.0%
6 13401
 
10.4%
2 927
 
0.7%
3 244
 
0.2%
5 244
 
0.2%
( 155
 
0.1%
) 155
 
0.1%
_ 142
 
0.1%
1 132
 
0.1%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2337451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 347631
14.9%
N 251533
 
10.8%
D 249016
 
10.7%
T 225992
 
9.7%
o 169401
 
7.2%
. 113193
 
4.8%
P 103651
 
4.4%
e 91053
 
3.9%
H 71843
 
3.1%
L 69152
 
3.0%
Other values (46) 644986
27.6%
Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:29.507824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length14
Median length7
Mean length6.164341082
Min length3

Characters and Unicode

Total characters2558331
Distinct characters37
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSystem
2nd rowNetwork
3rd rowNetwork
4th rowNetwork
5th rowNetwork
ValueCountFrequency (%)
network 269590
65.0%
web 110717
26.7%
advertisement 13532
 
3.3%
system 6160
 
1.5%
socialnetwork 4077
 
1.0%
cloud 2740
 
0.7%
download 2608
 
0.6%
media 1922
 
0.5%
unspecified 1156
 
0.3%
vpn 611
 
0.1%
Other values (14) 1908
 
0.5%
2023-09-28T14:58:30.359470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 436468
17.1%
t 307680
12.0%
r 287843
11.3%
o 286840
11.2%
w 276279
10.8%
N 274278
10.7%
k 273982
10.7%
b 110995
 
4.3%
W 110717
 
4.3%
i 23088
 
0.9%
Other values (27) 170161
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2137438
83.5%
Uppercase Letter 420893
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 436468
20.4%
t 307680
14.4%
r 287843
13.5%
o 286840
13.4%
w 276279
12.9%
k 273982
12.8%
b 110995
 
5.2%
i 23088
 
1.1%
d 22071
 
1.0%
s 21077
 
1.0%
Other values (12) 91115
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
N 274278
65.2%
W 110717
26.3%
A 13539
 
3.2%
S 10703
 
2.5%
C 3859
 
0.9%
D 2609
 
0.6%
M 2060
 
0.5%
U 1160
 
0.3%
V 843
 
0.2%
P 734
 
0.2%
Other values (5) 391
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2558331
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 436468
17.1%
t 307680
12.0%
r 287843
11.3%
o 286840
11.2%
w 276279
10.8%
N 274278
10.7%
k 273982
10.7%
b 110995
 
4.3%
W 110717
 
4.3%
i 23088
 
0.9%
Other values (27) 170161
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2558331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 436468
17.1%
t 307680
12.0%
r 287843
11.3%
o 286840
11.2%
w 276279
10.8%
N 274278
10.7%
k 273982
10.7%
b 110995
 
4.3%
W 110717
 
4.3%
i 23088
 
0.9%
Other values (27) 170161
 
6.7%

application_is_guessed
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06491478744
Minimum0
Maximum1
Zeros388080
Zeros (%)93.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:30.718220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2463757376
Coefficient of variation (CV)3.795371552
Kurtosis10.47437241
Mean0.06491478744
Median Absolute Deviation (MAD)0
Skewness3.531900612
Sum26941
Variance0.06070100408
MonotonicityNot monotonic
2023-09-28T14:58:30.977051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 388080
93.5%
1 26941
 
6.5%
ValueCountFrequency (%)
0 388080
93.5%
1 26941
 
6.5%
ValueCountFrequency (%)
1 26941
 
6.5%
0 388080
93.5%

application_confidence
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.664233858
Minimum0
Maximum6
Zeros1156
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:31.199480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.251893644
Coefficient of variation (CV)0.2210172947
Kurtosis10.23282866
Mean5.664233858
Median Absolute Deviation (MAD)0
Skewness-3.486396961
Sum2350776
Variance1.567237695
MonotonicityNot monotonic
2023-09-28T14:58:31.441635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 386897
93.2%
1 25882
 
6.2%
0 1156
 
0.3%
3 859
 
0.2%
4 200
 
< 0.1%
5 27
 
< 0.1%
ValueCountFrequency (%)
0 1156
 
0.3%
1 25882
6.2%
3 859
 
0.2%
4 200
 
< 0.1%
5 27
 
< 0.1%
ValueCountFrequency (%)
6 386897
93.2%
5 27
 
< 0.1%
4 200
 
< 0.1%
3 859
 
0.2%
1 25882
 
6.2%
Distinct14929
Distinct (%)4.2%
Missing55644
Missing (%)13.4%
Memory size3.2 MiB
2023-09-28T14:58:31.861583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length79
Median length70
Mean length20.17854788
Min length3

Characters and Unicode

Total characters7251706
Distinct characters43
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique740 ?
Unique (%)0.2%

Sample

1st row239.255.255.250:1900
2nd row_googlecast._tcp.local
3rd row_googlecast._tcp.local
4th rowmobile.slashdot.org
5th rowaccounts.google.com
ValueCountFrequency (%)
www.facebook.com 4328
 
1.2%
star-mini.c10r.facebook.com 3932
 
1.1%
email.seznam.cz 3865
 
1.1%
e8218.dscb1.akamaiedge.net 3827
 
1.1%
192.168.1.223 3018
 
0.8%
www.google.com 2667
 
0.7%
detectportal.firefox.com 2519
 
0.7%
scontent.xx.fbcdn.net 2262
 
0.6%
d2tpbry8f62bv9.cloudfront.net 2144
 
0.6%
ib.adnxs.com 2004
 
0.6%
Other values (14917) 328820
91.5%
2023-09-28T14:58:32.641495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 836636
 
11.5%
o 575750
 
7.9%
e 546234
 
7.5%
c 538328
 
7.4%
a 471692
 
6.5%
t 408543
 
5.6%
m 390078
 
5.4%
s 353642
 
4.9%
n 333826
 
4.6%
i 291809
 
4.0%
Other values (33) 2505168
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5898556
81.3%
Other Punctuation 838156
 
11.6%
Decimal Number 409011
 
5.6%
Dash Punctuation 105844
 
1.5%
Connector Punctuation 120
 
< 0.1%
Space Separator 9
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 575750
 
9.8%
e 546234
 
9.3%
c 538328
 
9.1%
a 471692
 
8.0%
t 408543
 
6.9%
m 390078
 
6.6%
s 353642
 
6.0%
n 333826
 
5.7%
i 291809
 
4.9%
d 262481
 
4.4%
Other values (16) 1726173
29.3%
Decimal Number
ValueCountFrequency (%)
1 93757
22.9%
2 65460
16.0%
0 42571
10.4%
8 34825
 
8.5%
3 34242
 
8.4%
9 31501
 
7.7%
5 29067
 
7.1%
6 28749
 
7.0%
4 24677
 
6.0%
7 24162
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 836636
99.8%
: 1520
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 105844
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 120
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5898556
81.3%
Common 1353150
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 575750
 
9.8%
e 546234
 
9.3%
c 538328
 
9.1%
a 471692
 
8.0%
t 408543
 
6.9%
m 390078
 
6.6%
s 353642
 
6.0%
n 333826
 
5.7%
i 291809
 
4.9%
d 262481
 
4.4%
Other values (16) 1726173
29.3%
Common
ValueCountFrequency (%)
. 836636
61.8%
- 105844
 
7.8%
1 93757
 
6.9%
2 65460
 
4.8%
0 42571
 
3.1%
8 34825
 
2.6%
3 34242
 
2.5%
9 31501
 
2.3%
5 29067
 
2.1%
6 28749
 
2.1%
Other values (7) 50498
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7251706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 836636
 
11.5%
o 575750
 
7.9%
e 546234
 
7.5%
c 538328
 
7.4%
a 471692
 
6.5%
t 408543
 
5.6%
m 390078
 
5.4%
s 353642
 
4.9%
n 333826
 
4.6%
i 291809
 
4.0%
Other values (33) 2505168
34.5%
Distinct47
Distinct (%)0.1%
Missing349959
Missing (%)84.3%
Memory size3.2 MiB
2023-09-28T14:58:32.965337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length47
Median length32
Mean length31.92980542
Min length5

Characters and Unicode

Total characters2077417
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row07b4162d4db57554961824a21c4a0fde
2nd row07b4162d4db57554961824a21c4a0fde
3rd row07b4162d4db57554961824a21c4a0fde
4th row07b4162d4db57554961824a21c4a0fde
5th row07b4162d4db57554961824a21c4a0fde
ValueCountFrequency (%)
0ffee3ba8e615ad22535e7f771690a28 30815
47.4%
1a5fe5677b0e4fbbc854e8908225637d 11342
 
17.4%
07b4162d4db57554961824a21c4a0fde 10070
 
15.5%
dda6c525431b3259dac349220160cdcb 7626
 
11.7%
61d0d709fe7ac199ef4b2c52bc8cef75 3038
 
4.7%
6b87ab76e189e2222a12ff9d643060cd 1164
 
1.8%
c5ec106b91c503167f57054ca38da945 170
 
0.3%
dfa6e4a7819410092c24975e860e1380 124
 
0.2%
1,3,6,12,15,28,42 123
 
0.2%
bb32cf215dd58fdee6573e65933e6c55 107
 
0.2%
Other values (37) 483
 
0.7%
2023-09-28T14:58:33.516684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 199634
 
9.6%
2 189225
 
9.1%
e 180099
 
8.7%
7 159622
 
7.7%
a 145956
 
7.0%
f 137619
 
6.6%
0 129690
 
6.2%
6 129374
 
6.2%
1 129135
 
6.2%
d 112462
 
5.4%
Other values (7) 564601
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1322951
63.7%
Lowercase Letter 752805
36.2%
Other Punctuation 1661
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 199634
15.1%
2 189225
14.3%
7 159622
12.1%
0 129690
9.8%
6 129374
9.8%
1 129135
9.8%
8 112445
8.5%
3 99081
7.5%
4 94333
7.1%
9 80412
6.1%
Lowercase Letter
ValueCountFrequency (%)
e 180099
23.9%
a 145956
19.4%
f 137619
18.3%
d 112462
14.9%
b 109789
14.6%
c 66880
 
8.9%
Other Punctuation
ValueCountFrequency (%)
, 1661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1324612
63.8%
Latin 752805
36.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 199634
15.1%
2 189225
14.3%
7 159622
12.1%
0 129690
9.8%
6 129374
9.8%
1 129135
9.7%
8 112445
8.5%
3 99081
7.5%
4 94333
7.1%
9 80412
6.1%
Latin
ValueCountFrequency (%)
e 180099
23.9%
a 145956
19.4%
f 137619
18.3%
d 112462
14.9%
b 109789
14.6%
c 66880
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2077417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 199634
 
9.6%
2 189225
 
9.1%
e 180099
 
8.7%
7 159622
 
7.7%
a 145956
 
7.0%
f 137619
 
6.6%
0 129690
 
6.2%
6 129374
 
6.2%
1 129135
 
6.2%
d 112462
 
5.4%
Other values (7) 564601
27.2%
Distinct262
Distinct (%)0.4%
Missing350487
Missing (%)84.5%
Memory size3.2 MiB
2023-09-28T14:58:33.989564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2065088
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st row303951d4c50efb2e991652225a6f02b1
2nd row76cc3e2d3028143b23ec18e27dbd7ca9
3rd row303951d4c50efb2e991652225a6f02b1
4th row76cc3e2d3028143b23ec18e27dbd7ca9
5th rowb898351eb5e266aefd3723d466935494
ValueCountFrequency (%)
303951d4c50efb2e991652225a6f02b1 8256
 
12.8%
76cc3e2d3028143b23ec18e27dbd7ca9 4511
 
7.0%
2b33c1374db4ddf06942f92373c0b54b 2840
 
4.4%
fbe78c619e7ea20046131294ad087f05 2543
 
3.9%
410b9bedaf65dd26c6fe547154d60db4 2467
 
3.8%
7bee5c1d424b7e5f943b06983bb11422 2262
 
3.5%
d199ba0af2b08e204c73d6d81a1fd260 2078
 
3.2%
8d2a028aa94425f76ced7826b1f39039 2050
 
3.2%
ab41313cfec25328b20865eb1388e0a2 1952
 
3.0%
b898351eb5e266aefd3723d466935494 1858
 
2.9%
Other values (252) 33717
52.2%
2023-09-28T14:58:34.752674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 175319
 
8.5%
1 152583
 
7.4%
3 138625
 
6.7%
b 138610
 
6.7%
5 137082
 
6.6%
0 133908
 
6.5%
e 133216
 
6.5%
d 132100
 
6.4%
9 131276
 
6.4%
4 124215
 
6.0%
Other values (6) 668154
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1327914
64.3%
Lowercase Letter 737174
35.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 175319
13.2%
1 152583
11.5%
3 138625
10.4%
5 137082
10.3%
0 133908
10.1%
9 131276
9.9%
4 124215
9.4%
6 120330
9.1%
7 110070
8.3%
8 104506
7.9%
Lowercase Letter
ValueCountFrequency (%)
b 138610
18.8%
e 133216
18.1%
d 132100
17.9%
f 115765
15.7%
c 111946
15.2%
a 105537
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1327914
64.3%
Latin 737174
35.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 175319
13.2%
1 152583
11.5%
3 138625
10.4%
5 137082
10.3%
0 133908
10.1%
9 131276
9.9%
4 124215
9.4%
6 120330
9.1%
7 110070
8.3%
8 104506
7.9%
Latin
ValueCountFrequency (%)
b 138610
18.8%
e 133216
18.1%
d 132100
17.9%
f 115765
15.7%
c 111946
15.2%
a 105537
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2065088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 175319
 
8.5%
1 152583
 
7.4%
3 138625
 
6.7%
b 138610
 
6.7%
5 137082
 
6.6%
0 133908
 
6.5%
e 133216
 
6.5%
d 132100
 
6.4%
9 131276
 
6.4%
4 124215
 
6.0%
Other values (6) 668154
32.4%
Distinct22
Distinct (%)< 0.1%
Missing368761
Missing (%)88.9%
Memory size3.2 MiB
2023-09-28T14:58:35.165256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length151
Median length102
Mean length72.78182015
Min length1

Characters and Unicode

Total characters3366887
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowMozilla/5.0 (X11; Linux x86_64; rv:43.0) Gecko/20100101 Firefox/43.0 Iceweasel/43.0.4
2nd rowMozilla/5.0 (X11; Linux x86_64; rv:43.0) Gecko/20100101 Firefox/43.0 Iceweasel/43.0.4
3rd rowMozilla/5.0 (X11; Linux x86_64; rv:43.0) Gecko/20100101 Firefox/43.0 Iceweasel/43.0.4
4th rowMozilla/5.0 (X11; Linux x86_64; rv:43.0) Gecko/20100101 Firefox/43.0 Iceweasel/43.0.4
5th rowMozilla/5.0 (X11; Linux x86_64; rv:43.0) Gecko/20100101 Firefox/43.0 Iceweasel/43.0.4
ValueCountFrequency (%)
mozilla/5.0 41091
12.1%
gecko/20100101 41080
12.1%
linux 22053
 
6.5%
x11 22031
 
6.5%
x86_64 22021
 
6.5%
rv:43.0 22016
 
6.5%
firefox/43.0 22016
 
6.5%
iceweasel/43.0.4 22016
 
6.5%
windows 19061
 
5.6%
nt 19061
 
5.6%
Other values (64) 87664
25.8%
2023-09-28T14:58:35.749365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 313770
 
9.3%
293850
 
8.7%
. 206881
 
6.1%
1 198712
 
5.9%
e 161992
 
4.8%
/ 157648
 
4.7%
o 150604
 
4.5%
i 132957
 
3.9%
4 110113
 
3.3%
l 108341
 
3.2%
Other values (56) 1532019
45.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1244601
37.0%
Decimal Number 952854
28.3%
Other Punctuation 477002
 
14.2%
Uppercase Letter 294279
 
8.7%
Space Separator 293850
 
8.7%
Open Punctuation 41126
 
1.2%
Close Punctuation 41126
 
1.2%
Connector Punctuation 22021
 
0.7%
Dash Punctuation 28
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 161992
13.0%
o 150604
12.1%
i 132957
10.7%
l 108341
 
8.7%
r 91067
 
7.3%
x 89216
 
7.2%
c 67237
 
5.4%
a 67232
 
5.4%
n 54777
 
4.4%
s 46510
 
3.7%
Other values (15) 274668
22.1%
Uppercase Letter
ValueCountFrequency (%)
M 41238
14.0%
G 41090
14.0%
F 41081
14.0%
L 26134
8.9%
I 22327
7.6%
X 22031
7.5%
P 20449
6.9%
W 19718
6.7%
T 19101
6.5%
N 19079
6.5%
Other values (11) 22031
7.5%
Decimal Number
ValueCountFrequency (%)
0 313770
32.9%
1 198712
20.9%
4 110113
 
11.6%
3 102258
 
10.7%
5 79248
 
8.3%
6 75373
 
7.9%
2 51264
 
5.4%
8 22055
 
2.3%
7 35
 
< 0.1%
9 26
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 206881
43.4%
/ 157648
33.0%
; 63193
 
13.2%
: 41080
 
8.6%
, 8200
 
1.7%
Space Separator
ValueCountFrequency (%)
293850
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41126
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41126
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 22021
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1828007
54.3%
Latin 1538880
45.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 161992
 
10.5%
o 150604
 
9.8%
i 132957
 
8.6%
l 108341
 
7.0%
r 91067
 
5.9%
x 89216
 
5.8%
c 67237
 
4.4%
a 67232
 
4.4%
n 54777
 
3.6%
s 46510
 
3.0%
Other values (36) 568947
37.0%
Common
ValueCountFrequency (%)
0 313770
17.2%
293850
16.1%
. 206881
11.3%
1 198712
10.9%
/ 157648
8.6%
4 110113
 
6.0%
3 102258
 
5.6%
5 79248
 
4.3%
6 75373
 
4.1%
; 63193
 
3.5%
Other values (10) 226961
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3366887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 313770
 
9.3%
293850
 
8.7%
. 206881
 
6.1%
1 198712
 
5.9%
e 161992
 
4.8%
/ 157648
 
4.7%
o 150604
 
4.5%
i 132957
 
3.9%
4 110113
 
3.3%
l 108341
 
3.2%
Other values (56) 1532019
45.5%
Distinct73
Distinct (%)0.2%
Missing372650
Missing (%)89.8%
Memory size3.2 MiB
2023-09-28T14:58:36.040850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length25
Mean length13.87682613
Min length1

Characters and Unicode

Total characters587975
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowapplication/ocsp-response
2nd rowtext/html
3rd rowtext/css
4th rowtext/css
5th rowtext/css
ValueCountFrequency (%)
text/html 5941
14.0%
image/gif 5595
13.2%
application/javascript 4130
9.8%
application/ocsp-response 4039
9.6%
text/xml 3921
9.3%
image/jpeg 3762
8.9%
text/javascript 3623
8.6%
application/x-javascript 2931
6.9%
text/css 2018
 
4.8%
application/json 1982
 
4.7%
Other values (54) 4343
10.3%
2023-09-28T14:58:36.667601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 65861
11.2%
a 61625
 
10.5%
i 56428
 
9.6%
p 52957
 
9.0%
/ 42287
 
7.2%
e 40584
 
6.9%
c 30787
 
5.2%
s 29345
 
5.0%
o 25511
 
4.3%
l 25301
 
4.3%
Other values (40) 157289
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 536978
91.3%
Other Punctuation 42446
 
7.2%
Dash Punctuation 7750
 
1.3%
Decimal Number 417
 
0.1%
Math Symbol 159
 
< 0.1%
Uppercase Letter 133
 
< 0.1%
Space Separator 92
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 65861
12.3%
a 61625
11.5%
i 56428
10.5%
p 52957
9.9%
e 40584
 
7.6%
c 30787
 
5.7%
s 29345
 
5.5%
o 25511
 
4.8%
l 25301
 
4.7%
x 24351
 
4.5%
Other values (15) 124228
23.1%
Uppercase Letter
ValueCountFrequency (%)
P 21
15.8%
G 19
14.3%
J 17
12.8%
E 17
12.8%
T 14
10.5%
M 10
7.5%
L 10
7.5%
H 7
 
5.3%
U 6
 
4.5%
F 4
 
3.0%
Other values (4) 8
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/ 42287
99.6%
. 143
 
0.3%
* 11
 
< 0.1%
, 5
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 389
93.3%
4 25
 
6.0%
8 3
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 156
98.1%
= 3
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 7750
100.0%
Space Separator
ValueCountFrequency (%)
92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 537111
91.3%
Common 50864
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 65861
12.3%
a 61625
11.5%
i 56428
10.5%
p 52957
9.9%
e 40584
 
7.6%
c 30787
 
5.7%
s 29345
 
5.5%
o 25511
 
4.7%
l 25301
 
4.7%
x 24351
 
4.5%
Other values (29) 124361
23.2%
Common
ValueCountFrequency (%)
/ 42287
83.1%
- 7750
 
15.2%
2 389
 
0.8%
+ 156
 
0.3%
. 143
 
0.3%
92
 
0.2%
4 25
 
< 0.1%
* 11
 
< 0.1%
, 5
 
< 0.1%
= 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 65861
11.2%
a 61625
 
10.5%
i 56428
 
9.6%
p 52957
 
9.0%
/ 42287
 
7.2%
e 40584
 
6.9%
c 30787
 
5.2%
s 29345
 
5.0%
o 25511
 
4.3%
l 25301
 
4.3%
Other values (40) 157289
26.8%

label
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003351637628
Minimum0
Maximum1
Zeros413630
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-28T14:58:37.230580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05779629921
Coefficient of variation (CV)17.2441969
Kurtosis293.3685221
Mean0.003351637628
Median Absolute Deviation (MAD)0
Skewness17.18624765
Sum1391
Variance0.003340412202
MonotonicityNot monotonic
2023-09-28T14:58:37.420067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 413630
99.7%
1 1391
 
0.3%
ValueCountFrequency (%)
0 413630
99.7%
1 1391
 
0.3%
ValueCountFrequency (%)
1 1391
 
0.3%
0 413630
99.7%